{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>175</td>\n",
       "      <td>925200</td>\n",
       "      <td>47175925200</td>\n",
       "      <td>9252</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>227429512</td>\n",
       "      <td>1667739</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-85.61516 35.76106, -85.61509 35.761...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>175</td>\n",
       "      <td>925000</td>\n",
       "      <td>47175925000</td>\n",
       "      <td>9250</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>480712883</td>\n",
       "      <td>1225717</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-85.60513 35.70854, -85.60511 35.708...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>003</td>\n",
       "      <td>950201</td>\n",
       "      <td>47003950201</td>\n",
       "      <td>9502.01</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>121774227</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-86.64406 35.64029, -86.64375 35.642...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>003</td>\n",
       "      <td>950202</td>\n",
       "      <td>47003950202</td>\n",
       "      <td>9502.02</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>110617191</td>\n",
       "      <td>700793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-86.66377 35.58189, -86.66367 35.582...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>093</td>\n",
       "      <td>003300</td>\n",
       "      <td>47093003300</td>\n",
       "      <td>33</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>5860088</td>\n",
       "      <td>229299</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-83.86208 35.99255, -83.86207 35.992...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        47        175    925200  47175925200     9252  Census Tract   G5020   \n",
       "1        47        175    925000  47175925000     9250  Census Tract   G5020   \n",
       "2        47        003    950201  47003950201  9502.01  Census Tract   G5020   \n",
       "3        47        003    950202  47003950202  9502.02  Census Tract   G5020   \n",
       "4        47        093    003300  47093003300       33  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  227429512   1667739  ...       71        0        0       71   \n",
       "1          S  480712883   1225717  ...        0        0        0        0   \n",
       "2          S  121774227         0  ...        0        0        0        0   \n",
       "3          S  110617191    700793  ...        0        0        0        0   \n",
       "4          S    5860088    229299  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0        0        0        0        0   \n",
       "2        0       26       26        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-85.61516 35.76106, -85.61509 35.761...  \n",
       "1  POLYGON ((-85.60513 35.70854, -85.60511 35.708...  \n",
       "2  POLYGON ((-86.64406 35.64029, -86.64375 35.642...  \n",
       "3  POLYGON ((-86.66377 35.58189, -86.66367 35.582...  \n",
       "4  POLYGON ((-83.86208 35.99255, -83.86207 35.992...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/tn_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1701 popn tracts for TN\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"TN\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population MD voter-age population\n",
      "state pop= 6177224 VAP pct Hispanic is  0.102230809839338\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP MD\n",
      "total state pop= 6177224 , VAP pct Black is  0.29158797491777083\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for TN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for TN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "612 4376 0.03886622884199991 nonPolygon tract no, pop, area\n",
      "0.00021106174249997716\n",
      "0.03865516709949993\n",
      "749 2640 0.003706850693999965 nonPolygon tract no, pop, area\n",
      "6.2864300000113605e-06\n",
      "0.0037005642639999538\n",
      "965 4695 0.026006760042500163 nonPolygon tract no, pop, area\n",
      "0.01943334505200004\n",
      "0.006573414990500123\n",
      "1230 3796 0.019827977167999965 nonPolygon tract no, pop, area\n",
      "2.958669999998504e-05\n",
      "0.019798390467999978\n",
      "1405 2989 0.013519196238999914 nonPolygon tract no, pop, area\n",
      "7.200703399992877e-05\n",
      "0.0001451611335000273\n",
      "4.0081106999996494e-05\n",
      "0.013261946964499961\n"
     ]
    },
    {
     "data": {
      "image/png": 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uVdj4VsPp6upqfvKTn/DSSy/xzi0juO97w+jTpw8LFizgkksuafIlbrrpJj766CMWL17Mq6++yqZNm0LZgrBTwheRyFdbDa/f2nj81ZqG3Q8//JCuXbty+eWXM3H2Vt5btpa0tDQyMzObfZkTT/Rm+cTFxREfH098fGzdqKVHvYtI5Hv/d/D5m43Hp13TsLtz505WrlzJihUrKH3+Js772fMU3Oto/niORk8++SSDBw9m4MCBHRh05FEPX0Qi3551kNYTxv8Qbl0HSWkNlzIzMznzzDPp3r07xw/PpWdKHYVzLoH9XwT8UgsXLuSJJ57gz3/+c6iijxhK+CIS+SqLIXMQTPof6NH0Kamnn346GzZsoLa2ltKTprHH14OsHQtg5bPNvszHH3/Mz3/+c1588UVSU1NDFHzk0JCOiES+2mqITw54KT09nRkzZpCXl0dNTQ3/e99vKFt9C9+5/THW7Spj7dq1TJ06lZkzZ3L11VcD8O1vfxuA+++/nzFjxoSqFWGnhC8ika+qtPFO2gCmT5/O9OnTvYM374RkY+GcO2HMVU3KrVmzpnnlGKIhHRGJbIUboHA9pGYEV/7DP3rbUZd3XExRSj18EYlcPh88dJr/wAVXp0cO1JR76+dLE0r4IhK5dq9s3O/b7EmrgWUOavkhKTFOCV9EItfez73tla/CoAnB1emRA5ve6biYopjG8EUkclWXedusE+EIN1I10a03HNwNLsghoBiihC8ikat0t7ftelzwdarLIalr8G8QMUQJX0QiV3I3b1vdhmfVVhRBanqHhBPtlPBFJHLVP7+2pjz4OhVFwU/hjDFK+CISuSpLvG1S1+DrKOG3SAlfRCJX6W5I6gYp3YOvU7EfUpsvjSxK+CISyTa91XqZw6mH3yLNwxeRyPTeA7B3A1gbHlLi8ynhH4F6+CISmVa/6G1TegRfp7oUnE8JvwVK+CISmW74wNuOmhZ8nfL93jZNY/iBKOGLSGSqv3Hqoz/B7tWtly/6ErZ+6O2rhx+QxvBFJPL9+Sw4bgT8+5uQHGCK5ldrYfaZjcdK+AGphy8ikatb38b9r9bA05c2L7P3c/jrRU3PKeEH1GrCN7MUM1tiZivNbK2ZzfSfv8vMdpjZCv9ragv1b/XXW2Nmz5pZSns3QkQ6qZs+gnE3wqWPe8dbF8OG+Y3X938BT3wLygqb1lPCDyiYHn4VMNE5NwoYDUwxs3H+a79zzo32v+YeXtHMjgf+E8h1zo0A4oE2fAIjIjEtpQdMuQe+djH8bBf0Hgkv/Qfs3Qgl2+Gv34LaCjj3zqb10nqGJ94I1+oYvnPOAfUrFyX6X21ZdzQBSDWzGiAN2NnWIEVESEqDac/AnDz40ziIS4D4RPjBa3DwkB7+iZMhTqPVgQT1r2Jm8Wa2AtgDLHDOfey/dLOZrTKzR82s2d9QzrkdwG+BrcAuoMQ5N//wcv7vca2ZLTOzZYWFhYGKiEisS+/vJX1X5823v/JV70lYvU5qLNNndNjCi3RBJXznXJ1zbjTQDxhrZiOA2cBgvGGeXcD9h9fzvwlcBAwC+gJdzOyKFr7HHOdcrnMuNzs7+yiaIiIxof84uHkZzFgOx/+Ldy69f+P1iXcGridtm6XjnCsG8oEpzrmv/G8EPuBhYGyAKucDXzjnCp1zNcDLwJkByomIBC9rMKTnNByu2VHCpVX/zcya6cz8x9owBhbZgpmlk21m6f79VLwkvt7M+hxS7GJgTYDqW4FxZpZmZgacBxQcc9QiIofYX1bNUncSj9V9nfc+3xvucCJWMDde9QGeMLN4vDeI551zr5vZk2Y2Gu8D3C3AdQBm1hf4i3NuqnPuYzN7EfgEqAU+Bea0fzNEJJadM7RxGHhncUUYI4lswczSWQWcGuD89BbK7wSmHnL8C+AXxxCjiEirfn7hyfzy9XWMOL4Ni63FGM1dEpFOYcwAb6JgZlpSmCOJXFpLR0Q6hdE56fz20lFMPKlXuENp3WdvwIAz27b0cztQD19EOo1LxvQjs0uE9/B3rYJnp8Hbvwr5t1bCFxEJpS/e9bYb5oFry6IFx04JX0QklDa9422Ltx7dM3uPgRK+iEio1FTCl4u99X7AWwQuhJTwRURCZdvH3uqep/pXmKk6ENJvr4QvIhIqm9/xVvkccBZg3gJwIaSELyISKpvegX5joUuWtxbQ3g0h/fZK+CIioVC+H3athMHnesfJPWDNS7DsMbj/JKir7fAQdOOViEhHK9/vfx6vgxO8hH/BQ+v55ItSfrjoBv7rnGQo2QqZJ/Doo49y3XXXUVNTA8D+/fu58sorKSkpYfTo0cyaNQszIy8vj6qqKpKTkxk5cmRQYaiHLyLS0ZY9AjuWQc+h3gNbgEf+5yZ+Mym5sUxqJpWVlbz88svk5DQu/Xzfffdx2WWX8d5771FWVsabb77ZcO2FF14gPz+fP/zhD0GFoYQvItLRtnwAx42Em5dCvDew0m/KD2H4txrLpKYza9Ysrr/+euIOeURjfn4+F154IQDf/OY3WbRoEQBmxrRp05g4cSJvv/12UGEo4YuIdLQDOyHrhKbn0jJh2Ne9/RPOpaioiEWLFjUk93pFRUWkp6cDkJ6ezr59+wCvd//+++/zxBNPcMMNN0AQ+VwJX0Sko1WWQGKXlq/vWM4999zDHXfc0exSRkYGJSUlAJSUlJCZmQlAz549AcjJyWHUqFEAyc0qH0YJX0SkIx3YBQd3g7WQbtMHQNUBNnzyAXfffTdTpkxh165dXHbZZQBMmDCBuXPnAjB37lwmTJiAc44DB7ybtkpLS1m9ejVAdWuhaJaOiEhH+tz/IWvvEU1OX3PNNSxevJiqsjKWrSvnlRl1cOMbYMaQIUN47rnnALjjjju48sormT17NqeccgqTJ0+mtraWc889l9TUVGpqarjrrruYNm1aXWuhKOGLiHSk2ipv26Nfk9MPP/xw48H7v4eFv/CGflLT2bixcY2drKws/vnPfzapGxcXx/Lly5ucmzZtWquhaEhHRKQDLY07xdvZuaLlQnU1TbcdRAlfRKSDPPTORp79+yveQW1l44WaSlj6COz/wjvesxYS0zr8CVhK+CIiHWTrvnIeSPqzdzD2GvDVeQ89WfBz+OdtMGu0l/x7DvPeEMr3dmg8SvgiIh3kl4PXA+CLT4HZZ8ELV8HiWbBkTmOhRyZB5iBv5cydn3ZoPEr4IiIdYd1rJL16DQBxV70O1aVQ8Bos+G9I7t5YrltvSEz19pOOMFe/HSjhi4h0hPd+621P/jZkD216bfg34foPvP3KA7Btibdfsr1DQ9K0TBGR9nZwj7cU8vFj4HtPwO/9M3USUuHWtd56+OD19Ld9BAlJ3nHOuA4NSz18EZH2tn2pt+05zPuQtvhL7/iW1Y3JHqD/Gd72i0X4EpMpTizp0LCU8EVE2lvVQW9bWAAz0xvPl+1pWq7/6Q27W/rGs3z59zo0LCV8EZH21uskb3v4rJuDXzU9HjIJBp1DbfYQvuif2uFhKeGLiLS31IzA57d+3PS4zynwg38Qf+NSMCMz8+wODUsJX0SkvaX3h6sXQPfjm57fszZgcbM4zj5rCaNO+UuHhqWELyLSEXLGwm3rOH3CG43nevRvsXhSUhZxcR07cVIJX0SkA31JGvcOvNo76N+x0y5b02rCN7MUM1tiZivNbK2ZzfSfv8vMdpjZCv9ragv1083sRTNbb2YFZnZGezdCRCRSZSUm8PsBV3LxRcvg5G+1XqEDBdPDrwImOudGAaOBKWZW/zb1O+fcaP9rbgv1HwTmOedOAkYBBccatIhItLg+JxuAzMTw3+faagTOOQf4J5WS6H+5YL64mXUHzgGu8n+taoJ4DJeISGcxY8BxnNQlhX/p3rHr5AQjqDF8M4s3sxXAHmCBc65+btHNZrbKzB41s0DzkE4ACoHHzOxTM/uLmQVstZlda2bLzGxZYWHhUTRFRCQyTerZg6yk8Pfwg0r4zrk659xooB8w1sxGALOBwXjDPLuA+wNUTQD+BZjtnDsVKAN+0sL3mOOcy3XO5WZnZ7e1HSIi0oo2zdJxzhUD+cAU59xX/jcCH/AwMDZAle3A9kP+IngR7w1ARERCLJhZOtlmlu7fTwXOB9abWZ9Dil0MrDm8rnNuN7DNzIb5T50HrDvWoEVEpO2CGVTqAzxhZvF4bxDPO+deN7MnzWw03ge4W4DrAMysL/AX51z9NM0ZwNNmlgRsBv6tfZsgIiLBCGaWzirg1ADnp7dQficw9ZDjFUDu0YcoIiLtQXfaiojEiJhL+F9tOcC6D3ZSV+sLdygiIiEV/omhIbTp0z3Mf2QtvlrH/h1lnPW9E8MdkohIyMRMD79wWynzH15L1/RkBozMYvWi7ZSVVIU7LBGRkImZhL9l1V58znHpT09j/HeH4Kt1fL70q9Yrioh0EjGT8Iu/KqdbRgopXRLJ6N2FXgO6KeGLSEyJmYR/sKiKblkpDcc5wzPZu+0gtTV1YYxKRCR0YibhV5RWk9I1seG4e3YqPp+jvESLd4pIbIiZhH+wuIou6ckNx746b4Xn+MSY+ScQkRgXE9muqqKWmso6umU0DunUVntDOXFxFq6wRERCKiYSflmRN/2ya0ZjD7/XgO4ArHpne1hiEhEJtZhI+D6fN3wTl9DYm+97YjpDTz+OT978UvPxRSQmxETCrx+2qR+3r9d7UA98dY49X5ZSuLU0HKGJiIRMbCR8f8/ed9j6OVn9ugIw90+reP7upVSW1YQ8NhGRUImJhJ/qn45ZcbBpQu87JJ0BI7IajvftOIiISGcVEwk/KTWB+IQ4yg6Zc/+LX/yCM888k/ue+0/iehcB8PQzT3Leeedx7rnn8swzzwBw1113MXz4cPLy8sjLy6OuTjdqiUh0ionVMs2MHr1SKd5dBsCKFStYsmQJixcvZtu2bVzxr9M5u/eVrNz0LgsXLsSs6VTNO++8kyuuuCIcoYuItJuY6OEDZB3flb3bvSGbDRs2MGbMGABycnL4cusWPv1iEYnxKUyePJmLL76Y7dsbp2ved999nHXWWcyaNSsssYuItIdOnfC/+mITa999i6rycnqf0J2DRVXs23GQESNGkJ+fT3V1NStXrmT79u1U1BWzd+9e5s+fz9VXX83tt98OwIwZM1i5ciULFizgtddeY9GiRWFulYjI0em0CX/Pls089ZMfMu9Pv+Ofs+7jxNOOIy7eKPhgFyeffDKXX345kyZN4sEHH+RrX/saqUldGTvqLMyMCy64gNWrVwOQlZWFmZGamsp3vvMdli9fHuaWiYgcnU6b8NfmLwRg0OgxbF29goQkx4ARWWxeUQjAjTfeyLvvvsttt93GyJEjGdzrFDZuWwfA8uXLGTx4MADFxcUAOOfIz89n2LBhoW+MiEg76LQf2m4vWEv/kaMZNfkbfLFiObs//4zeJ/Tgi5V7qSyr4VsXf4Pa2lqysrL4wx/+yIu/WEPh7vXk5eXh8/mYM2cOALfccgufffYZzjny8vKYOnVqmFsmInJ0Om3CL91XSJ8Th9IzZwAAxXt20zPneAD2bj/I/Pnzm5RPTC7g5uk/ZfwlTZ9z+/jjj4ckXhGRjtYph3Rqa2qoKD1Al4xMaqu9dXISk5LpkZ0GQOm+imZ1umakULyn+XkRkc6iUyb88mLvRqquGVlUHDgAQGr3HnT1r4d/sKj5Ymm9BnbTejoi0ql1yoR/sGgfAF0zMikvLQEgtVt34hPjSO2WyMHi5gk/tUsSVWU1OOeaXRMR6Qw6ZcIv2eM9nLxLRmZDDz+tew8AktMSqa6obVan6Kty0nunNbvLVkSks+h0Cd85x4r5c+ma1ZOsfjlUlXvLKSR36QJ4jzSsrfY1q1e0u4yM49JCGquISCh1uoS/be0qdn62jrEXXUJ8QuNDy53PS/KJSXHU1TRdAK2muo7SfZVk9OkS0lhFREKp0yX8D196li4ZmYw8dzIAXTO95Y9L9+/DV+ejdF8liSlNZ6NW+dfBT+ueFNpgRURCqFPNw9+xfh3b163h3B9cQ0KSl7y798wGoLRwD3u3J1JWUs2w03sfVlPj9iLS+XWqhL9r42cADD/7XJzPh8XFkdHHu9lqx4ZNrF9STq+B3Rl4Ss8m9epn5ugDWxHpzFod0jGzFDNbYmYrzWytmc30n7/LzHaY2Qr/q8U1B8ws3sw+NbPX2zP4w1WVl4MZK+fP5Q//dhmfzvsHaT3SSe7ShdX5q6mt8XHelcMbnnFbr67GG9+PT+x0I1wiIg2C6eFXAROdcwfNLBF438ze8F/7nXPut0F8jR8CBUD3o4wzKJUHD5CclsbyN16jprKCdx5/mN5DhlJVVkady+L87w8hs2/zD2Zr/R/iJiQp4YtI59VqhnOe+oe9JvpfQd+dZGb9gG8AfzmqCNugorSUmspKKksPMPysPJzzsWvD+obrg0ZlB6xXP00zITG+o0MUEQmboLq0/iGZFcAeYIFz7mP/pZvNbJWZPWpmGS1U/z1wB9B88ns7qyg9gMV5TRqcezqJySns37mD+IQUXF0hy+dt4cu1+ygraXqnbW21v4evIR0R6cSC+tDWOVcHjDazdODvZjYCmA38Eq+3/0vgfuDfD61nZhcCe5xzy80s70jfw8yuBa4F6N+/f5saUa+ytJSULl0pKy4iPjGJAaeM5vMli8keOICi3ftZ9fZ2Vr3tPbowISmOhKR4EpLiiE+I859TD19EOq82zdJxzhWbWT4w5dCxezN7GAj0gex44Fv+D3RTgO5m9pRzrtkTwZ1zc4A5ALm5uUe1oM2eLZtITvPG6CtKSxg1aSobl35EVdlB4hMTufr3Z7F/VxmFW0spK66itsZHbVUdJYUVmBnds1OO5tuKiESFVhO+mWUDNf5knwqcD/yvmfVxzu3yF7sYWHN4XefcT4Gf+r9OHnB7oGTfnuqXUigvLmbEhPPpcVxvSr7azcDRuaR2S+L4bkkcP7Sl0ScRkc4rmB5+H+AJM4vHG/N/3jn3upk9aWaj8YZ0tgDXAZhZX+AvzrmQPxrK4uLI6N2X8gMllO4rxOLi+OatP+XLVZ8yerKeVCUisa3VhO+cWwWcGuD89BbK7wSaZVfnXD6Q3+YI2+DMSy7ng+efAqC22lsu4bhBgzlu0OCO/LYiIlGhU91pe+rXv+lfC98Y++1Lwh2OiEhE6VQJPzmtC+f/x03hDkNEJCJp4rmISIxQwhcRiRFK+CIiMUIJX0QkRijhi4jECCV8EZEYoYQvIhIjlPBFRGKE1T/PNZKYWSHwZQd+i57A3g78+qGm9kS+ztYmtSfyDHDOBX7Kk19EJvyOZmbLnHO54Y6jvag9ka+ztUntiU4a0hERiRFK+CIiMSJWE/6ccAfQztSeyNfZ2qT2RKGYHMMXEYlFsdrDFxGJOUr4IiIxIqYSvpmNMrMPzWy1mf3DzLofcu2nZrbRzD4zswvCGWewzGy0mX1kZivMbJmZjfWfTzKzx/ztXOl/gHzEO0J7Es3sCX97Cszsp+GONRhHaM+/+s/Vv3z+50NHtJba4792iv//1lr/zyklnLEG4wg/n4FmVnHIz+fP4Y613TjnYuYFLAUm+Pf/Hfilf/9kYCWQDAwCNgHx4Y43iPbMB77u358K5Pv3bwIe8+/3ApYDceGO9xjacznwN/9+GrAFGBjueI+2PYeVGQlsDnesx/jzSQBWAaP8x1lR/v9nILAm3PF1xCumevjAMGCRf38B8F3//kV4CaXKOfcFsBEYG6B+pHFA/V8pPYCd/v2TgbcAnHN7gGIgGm4qaak9DuhiZglAKlANHAh9eG3WUnsO9X3g2ZBFdGxaas9kYJVzbiWAc26fc64uDPG1VTA/n84l3O84IX5HXwxc5N+/DSj17/8RuOKQco8Al4Q73iDaMxzYCmwDduDdWg1wLfACXs9rEF7C/2644z2G9iQCfwMKgTLg2nDHeiztOazMJmBEuGM9xp/PLcCTwJvAJ8Ad4Y71GNsz0P979inwLnB2uGNtr1eneog5gJktBHoHuHQn3jDOLDP7b+A1vJ4igAUoHxHzVVtpz3nArc65l8zse3hvVOcDj+L9Mi/DW5NoMVAbmoiP7CjbMxaoA/oCGcB7ZrbQObc5RGG36CjbU1/3dKDcObcmJMEG4SjbkwCcBZwGlANvmdly59xbIQq7RUfZnl1Af+fcPjMbA7xiZl9zzkXDX5VHFLPz8M1sKPCUc25s/YeAzrl7/NfeBO5yzn0YzhhbY2YlQLpzzpmZASXOue4Byi0G/sM5ty7kQbZBS+0xs4eAj5xzT/rLPQrMc849H854W9Paz8fMfgcUOufuDluQbXCEn880YIpz7ip/uZ8Dlc6534Qx3Fa14f9PPnC7c25ZqGNsbzE1hm9mvfzbOOC/gPpP318DpplZspkNAk4EloQnyjbZCUzw708EPgcwszQz6+LfnwTURnqy9wvYHrw/uyeapwswDlgfhvjaqqX21P8OXoo3VBUtWmrPm8Ap/t+7BH+ZqP19M7NsM4v375+Alw/C/tdke+h0Qzqt+L6Z3eTffxl4DMA5t9bMnsf7Ja0FbnLR8aHTNcCD/v9klXhj9+DNzHnTzHx4Y5PTwxRfW7XUnofwflZr8IbfHnPOrQpPiG3SUnsAzgG2R8KwVBsEbI9zrsjMHsCbBeeAuc65f4YvzKC19PM5B/gfM6vFG0q83jm3P0wxtquYHdIREYk1MTWkIyISy5TwRURihBK+iEiMUMIXEYkRSvgiIjFCCV9EJEYo4YuIxIj/A4ZJT9NDLlAqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69edb6b2-7b88-43ca-8017-ff4d271ce3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for TN)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>VTD</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREIWES</th>\n",
       "      <th>G20PREIBLA</th>\n",
       "      <th>...</th>\n",
       "      <th>G20USSDBRA</th>\n",
       "      <th>G20USSIHOO</th>\n",
       "      <th>G20USSISTA</th>\n",
       "      <th>G20USSIMCL</th>\n",
       "      <th>G20USSIJAM</th>\n",
       "      <th>G20USSIHIL</th>\n",
       "      <th>G20USSIGRU</th>\n",
       "      <th>G20USSIHEN</th>\n",
       "      <th>G20USSIFAP</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>033</td>\n",
       "      <td>8</td>\n",
       "      <td>1611</td>\n",
       "      <td>467</td>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON Z ((-89.05690 35.81323 0.00000, -89.05...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>033</td>\n",
       "      <td>6</td>\n",
       "      <td>1612</td>\n",
       "      <td>473</td>\n",
       "      <td>102</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-89.02185 35.85440 0.00000, -89.02...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>035</td>\n",
       "      <td>Mayland</td>\n",
       "      <td>1714</td>\n",
       "      <td>1028</td>\n",
       "      <td>226</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>206</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON Z ((-85.23575 36.10575 0.00000, -85.23...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>009</td>\n",
       "      <td>Martin Luther King</td>\n",
       "      <td>0411</td>\n",
       "      <td>394</td>\n",
       "      <td>642</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>585</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON Z ((-83.96293 35.78866 0.00000, -83.96...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>009</td>\n",
       "      <td>Everett</td>\n",
       "      <td>0413</td>\n",
       "      <td>836</td>\n",
       "      <td>508</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>469</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON Z ((-83.95053 35.78375 0.00000, -83.95...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP                NAME   VTD  G20PRERTRU  G20PREDBID  \\\n",
       "0      47      033                   8  1611         467          93   \n",
       "1      47      033                   6  1612         473         102   \n",
       "2      47      035             Mayland  1714        1028         226   \n",
       "3      47      009  Martin Luther King  0411         394         642   \n",
       "4      47      009             Everett  0413         836         508   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PREIWES  G20PREIBLA  ...  G20USSDBRA  \\\n",
       "0           5           0           1           0  ...          76   \n",
       "1           2           0           1           1  ...          89   \n",
       "2           5           3           1           1  ...         206   \n",
       "3           9           1           2           0  ...         585   \n",
       "4          34           5           4           7  ...         469   \n",
       "\n",
       "   G20USSIHOO  G20USSISTA  G20USSIMCL  G20USSIJAM  G20USSIHIL  G20USSIGRU  \\\n",
       "0           1           1           2           0           0           1   \n",
       "1           0           1           0           0           0           0   \n",
       "2           5           0          10           1           0           2   \n",
       "3           5           3           2           1           7           4   \n",
       "4           6           0           6           1          13           5   \n",
       "\n",
       "   G20USSIHEN  G20USSIFAP                                           geometry  \n",
       "0           2           1  POLYGON Z ((-89.05690 35.81323 0.00000, -89.05...  \n",
       "1           1           0  POLYGON Z ((-89.02185 35.85440 0.00000, -89.02...  \n",
       "2           3           1  POLYGON Z ((-85.23575 36.10575 0.00000, -85.23...  \n",
       "3           8           5  POLYGON Z ((-83.96293 35.78866 0.00000, -83.96...  \n",
       "4          11           7  POLYGON Z ((-83.95053 35.78375 0.00000, -83.95...  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/tn_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1961 0.6182777037206635 2996186 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see NJ code for convex-hulling tracts\n",
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5520a7c2-0560-421c-b5ef-bfd8aa9752b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later\n",
    "lakeTracts = [0]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 9999:  #for Tennessee, these ALL appear to be interior\n",
    "            if lakeTracts == [0]:\n",
    "                lakeTracts = [m]\n",
    "                isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if y > 34:  #m==99940 or m==999909: \n",
    "                    thisTract = \"interior\"\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dca817fc-8a1a-4037-bb00-3f0e2e871491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic TN map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-82.1,35.9)\n",
    "pt2 = Point(-81.5,35.9)\n",
    "pt3 = Point(-81.5,36.8)\n",
    "pt4 = Point(-83.0,36.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  93 tracts w pop 358865.0  to reassign to tracts...\n",
      "[386, 393, 922, 923, 1234, 1319, 1560, 1604, 1610, 1613]\n",
      "I have flagged  91 precincts to reassign to precincts...\n",
      "[636, 1817, 1822, 1823, 1833, 1839, 1842, 1874, 1897, 1910, 1913, 1915, 1943, 1946, 1947, 1949]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1: #and tractGeom[t].centroid.x > -76.4 and tractGeom[t].centroid.y > 38.4:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1:# and vtdGeom[p].centroid.x > -76.4 and vtdGeom[p].centroid.y > 38.4:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  358865.0 people (VAP of 291213.0 ) and  164483.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for western MD\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-80,40)\n",
    "pt2 = Point(-80,39)\n",
    "pt3 = Point(-78,39)\n",
    "pt4 = Point(-77.5,40)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  149 tracts w pop 201388.0  to reassign to tracts...\n",
      "[557, 558, 564, 565, 566, 568, 569, 570, 573, 575, 576, 585, 664, 668, 818, 825, 826, 1524, 1525, 2522, 2524, 2528, 2981, 2984]\n",
      "I have flagged  92 precincts to reassign to precincts...\n",
      "[649, 650, 652, 658, 664, 670, 672, 673, 675, 676, 681, 690]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q7+3gWlzNLHv5SwZVZLAxbTen3nIhdsuRfeNXvnsJY7bNB6DMYMRrsCCFggGJ2e/FHPBhkGrIvVRRcOr0uPUmvAYzLnMU3ohE9DF9sMYPICY5l4Re49Afag9C1Wb46KaQiWfKAyF34rhs0LXNBOb0O1lStoQFJQtYXLqYZl8zBsXAuJRxTE2fyuRek0my/bSZLqgG2Vy3meUVy9lUt4mCxgKKW4pRpUqMKYarB17NTUNvwqC03QR3JDQTkMYJidvh482HluH3/rSBSqcEUKWClKHRZoS5haSYJgJeP6UNKQSkGYUAyVHVpGebSR8/jITB/VCUnulJ5Nu4Dk/+TvxVLvzNJgKBBA43uVeUJmx9HNjOHI8+I3PvcdUfoPqf3+AuqUOxxKJTjt3dNWh2IHMcKGYDOrOZqm1eEqti2Rq5m363TKR3Yp8j1rFu8V8IVqzG0LAb1duMUIP4kASMNoQ5Cp3RjioUlIAXxduC3tuC0e8iyusiwefZ7xfwCMFus43qyCT8UWnYzTHYmyuxtVTTq74Il97Eq/0nUJqQTaQxkihTFNGmaKLN0USbovd+N+lMOHwOqt3VbKvfRl5lHisqVuBX/USZopiUNompGVOZkDoBm+HgAca2+m3cMvcWGr2NAGRGZpIdnU3f6L6MShrF6OTR7drx70FTABonHLWlLSz7tIDiTfXYY0xMvWYA9RVOXM0+FEUQmWAhLSeGqISfPCcCLieVy5ZSsqaIkhIjNd6Qh4lJcdIrvpb4BBVbXATWuBhs8ZFYYqOwxMai2GPaPCLsSKQq8RfsxrdxK/7iBnz1OvzeRECHTqnGYHdiSDBhSLIhDAHwB5GBABIdIioRY7/e6HqlIw4wxXhLqqn+x1KEKY6Aqxycjfi2fUPQXYeaYcU0aSQxky9AMVlR3T5UTwDV40fqVFBVgn4PQb8X1e9F3WRA744FVBTVdFAbiuyVnPrw5R36O6lBP3U1m6mrWIOzegOiajMR9UUktdQQGfTvva7YYGBZZCxfJ/fFa4nE5XfR5G2iydeEeqjYSPuQEZHBlPQpTEmfwojEEeiVQyteh8/BlV9fSVFzEQ+Pe5jpvacTZ2k1h1a7oykAjR5NyZZ61i8oRaqStP4xxPeys2tNDRsXlwHQd0QCgyen0SsnBtHWkbyUuHdvpXRpPiXbWyipTcQRaH1hzyDcGBU3RsWHQR/AaAhiMkmiolVie8WQlDuQqJwBB3Wsx4va0oJv3Qa828vwVQTwOeKRMmTKUGjBYKnGlGbAft6pKEkZx7wfovSBd4FeAJgym4m57jQaln9Fw9LZeBeuRV8aQDWBMrUPaT9/hMiBpxyxTilV/N4mfM111C9dDysjUIIh2RP/OBaj6WDl0BlIrwOHtwmzJRaDoXW3SlWqtPhaaPI20ehtpNHbSJO3CU/QQ4QxglhTLP1i+hFjPvxCsDvg5rvd3zF391yWli3Fp/pIs6fxzrnv7HX57Aw0BaDRY6ne3cyHfwk9d71JRyBs7jFa9Pjcgf2unXxVf4ZMOnjDUlsJeP24yktxVlTirG/B3eTB3eLD5/bh8wTxeSV+n8TnU/D4DDT7YlHDxgaL0oTBVEBGrouEnNHYbHaE3oCiN6LoDKEZhKIPhY2QKkIGkcEg/oAfny+A1+/H73QR+6OK1dCA6tDj9yURStsRxKAvxxjjwJRuxzioL7qcYQjjsfmF70vd/+biWh9AGEO2efsEhegLJu49r6pBapZ+QN07r8HiEkQAmJROr9/8mYjstu/BeOmr29m9QyEhAu655bnjlrs74/Q7eW/re7yx+Q3qPfUkWhOZ0XsGMzJnkJuQu3ejV2eheQFp9Gh0eoVgQN3b+esMCvG97FTsbGTPGEZnUEjNjj6u++hNBiL79CGyz5Ft1ABBf4DGbVupXLuFDeu81LWMZEdeCy1rNhKtKydaX06KYQtxhrYlA/Gpfaj2PYcXOwZLGZFZRRizkzHmDkOJm3IcLWud+vcX4t5qQRjBkFhLwu3noZj2N3Upio6kU68k6dQrcZZvpvj5h+DjrZQuvhbdNWPIefiNw94jqAb588Ib+KBuLWOy4nj47C/bvR3dBSklH27/kGfXPEuTt4mJqRO5eejNjEoa1emdfls4ogIQQpiBxYApfP1HUspHhRC5hNJA2oEi4GopZXMr5aOBV4AhgARuklIuE0L8AbgVqAlf+qCU8psDy2uc3Cx6dxsbF5URk2zF4/TjdQdQA5KgX+Xsnw9F0Qu8rgCKTmCNNB7kXtfR6Ax64oYMIW7IEAZdLflmzi42LNpFffNAzN4R6NAREH7Gn1WIIgMINQBCQQoFKXRIoaDT6TEaDBgMevQGM5WrVfqUK7jdqWwtV3ArNpIiVAZFBDEZj30z0IHUvz0X5xoFodcRdU4kEZNOO+z1PlcDzduWoYuMYM+Se/CtlaGEsIeguqWQ+767njXNDUyN78XTZ36K8QSNCNrkbeLRpY8yv3g+45LHcffIuxma0L3dko9oAhKh/1E2KaVDCGEAfgTuBp4D7pNSLhJC3AT0kVI+0kr514EfpJSvCCGMgFVK2RhWAA4p5dNtFVYzAZ18vPdYHnVlzlbPpZ6TTmycmfhYC/FxFmJizBgN7ddBHi/BQJB/37kQp03Hg09POaqy+T8WU7W2iuhKDxlhK1cDkiqrgrQZwGZAH2XCEmMiIs5KTJKV+CQ7RtORJ/Xe3VXU/Pt70PVCemuJv3U4OruBxq9W4C/1YkiJxH5qf/R946hZ8C4NPyxA7qxCt7MaEQgp2GCyAWLNpDz4e+JGn3dw29UA/8v/PS9t/RKvKpmVM51fjPtHm/cB9ETumH8HS8qXcM/Ie7h20LXdasR/zCYgGdIQjvBXQ/glgf6EZgYA84A5wH4KQAgRCUwCbgjX5QN8x9QCjZMKX0Dlo/xSPjN4GKSo6BWBIgTGgMQiQ51Q+TcllO9TRiLxCPDpBapBIMw6DDY9cZmRzJjWm/SUo4/Nfjx8PbcQvRSk5B69p8eoUzPg1AwA6qsdbF9ejmd7A5YmHxG1XqJqvOj4STGqQHX48+Z+Ecy4eXir9Zbe8wSYJ4EutOCL0FH7yhbQWxFKAjLgxVdmov79CqACKQcTrR8KA4AB4DFsJ+0XU7CkHbyDdQ/5JV/z+LI/sNPtIcdq4dEJjzMsbcZR/wY9BSklyyqWsah0EdcNuo7rB1/f1SK1mTatAQghdEA+kA08L6XME0JsBC4APgcuB1qL2JRFyMTzWthklA/cLaXc85d7hxDiOmAV8GspZcOBFQghZgGzADIyMo6mbRo9lLxddTz02UZ2VjsYkBzBmF8M5txhKXvPO1x+KqqdVNe6qKtz09ToxdXiw+cMoLgC6Fx+cAdRHAEMDX48JR4++6EKh11Hv4kpXHxBP3S6jh+dbVpaQSRQsa3xuOqJTbQz/oL9s4X5/EHqql3UVzloqXPhqvdAYTM5jQFUT6DVeqSq4qsUGDND31V3JULxI/Qq+jgftlP6Y5vQn+I/f4HeFVJaPmMJJn/vvXWY/Tk0Fe8mIFVM0QkY7dF7z9U5S/jrD3cwp6oAmyK4b/CFXDvyjyjHEcOmO+MNevmi4Ave3fouOxp2EGuO5dysc7tarKPiqLyAwvb8T4E7gQDwLKGE718Ad0kp4w64fjSwHJgYVhrPAM1SykeEEElALaHZxGNAipTypsPdXzMBnfi8+mMhj329mbRoC3+6cDBT+ycel11fVSWr11Sx5IcSfDtasAbBrQOlt5Uxk9KZODal3c0Sfn+Ql/63HvJD4xl/jp17fjW2Xe/RGvMfX0KqI0jy/aOJiWs9eJq3pARDYiJKG10wVVWleul8XFuqMexKRcifxowBcyMpD01j6e6PmbvrC+ZXbcOjSs5Kzua3pz5HnK2bRfFsR2rdtdz1/V1sqN3AgNgBXDXgKs7uczbmbrq+0W5uoEKIRwHnvrZ7IUQO8JaUcuwB1yYDy6WUmeHvpwEPSCnPPeC6TOArKeVhg11rCuDExekNMPrx73D7gyRFmvj75cMxGxQkoU5claGptgRUuc93uf93X1DFH1Q5b1gqhgNG+R5vgHc/2orrh+r9jjcnGrjjN+OIsBsPK6PLF8Bq/KkDDAZVnE4/Lqcfp9NPQ5OHpmYv6z4owCQFjkgd19wzkrTUjjc9bVlTQcT7O2lB0mBSUAAE7FGdHr3AbVAImnUImwGd3YgpwoDNakQqPgL6WowiiN/jJegNEPT4CfhDLzWgQlCi+KFXZTRR7lB7Xk17i48jl6Ai0CEZE5PAL0bcz6j0czq8vV2FlJJ5u+fx1MqnaPI28cSpT3BG7zM63fngaDnmNQAhRALgDy/cWoDpwJNCiEQpZbUQQiHkB/CfA8tKKSuFECVCiP5Sym3A6cDmcL0pUsqK8KUXAxuPuXUaPZ7Hv96C2x/yLalq9nLNf/OOq77kSAun9N3f9m426bFZ9ByYtTey2s/L9//ATX+dSHRU6yM4ty/Ir+77nqE+PX5CgyYDrf+nN4WPj72gT6d0/gDWCBPbrAqoKioSlVDeEgkIKbF6IMkVJLLBjwnvYWoyhV/7ExAB/CJIQASpMzRSZC2iKGM7F9mHMixxNFOzriTWduImf98T5O1fq//F+pr19IvpxzNTn+mUgG0dSVvWAFKA18PrAArwgZTyKyHE3UKI28PXfAK8BiCESAVekVLuGQbcCbwd9gDaBewJO/iUEGI4ob/RIuDn7dAejR7K4xcNwW7ScUrfOIQILfgKQBECRQBiz+fQdyHYe53bF+Q3H6+jpN5NnM3IvWfkMD6r9V2Wl12Uw9dRJirKWqivchFREFIHQoJef2hTkEEn2BMYwIDAkWxExJjRmxR0Jh16kw6bzUBUpAm71UCkzcDwIYnt+yMdht7ZsfT+/cTDXuP0Oflm+1Lmb19OcdU2zDShl3qEO5trhpxBVlIcRosZs8WC2WrFarJhMVsReuWgEW4ucCF3dmCLup7dzbuZWzSXVVWrWFu9FlfARbwlnj+c8gcuzL7wsGEfegraTmCNHk1JvYtb31jF9qoWHjxnIDef2ueI03Gn08c7b2+mcW0dZhW8fe3c/PNcYiKPbL9995OtVM0tw6eDidcNZOK47jvqdfvdzC3I45udP7KxfjXNagEIFSl1GAK9yY0bza/GX8uwlMyuFrXbsKZ6DR9u+5BVVauocIYMFNnR2XsDtU1InUCk8djSP3YlWigIjROOvF113Pb2agJBleeuGsnknMPno21q9PDhu1to3tCASYXGSIUzfzaA0SOTj+q+y/MrWPTaFiwBiT43hmkz+jCg7+FjwnQWNc4W/rH0XZZWfk99cCuIIFIqKL50MqzDmJg2jksHnUq/xNhub7fubPxBPyPfGgnAGb3PYFTSKKZnTN8vlHNPRVMAGicM/qDKG8t289fZW0iPtfLKdaPJSrAf8nqvL8D//rse9/oGDFJQHakw9pxMzpnc+5g7wYoaJ/99ehUxTaF1i2YD6NOsjDk1jckTe3V651pQV8VDC55lU/M80LnBn0iacSSnpI7nkkGnMiTl+LypuguqVFlbvZbV1aspaiqiydtElasKb9CLIhR0QofNYCPOEkeCJYEBsQPITcild2TvVlMqSinZUr+Fz3d+zjeF39DobaR/TH8+PP/DE+L32oOmADR6PKoqmbeliidnb2VXrZNpAxL55xXDibIcOkxzc7OXfz/6I1FuQW20wsQLszl9fFq7/OeWUrJxSx3LlpRSu7MJW1MAPQKHTWHIGRlMm5aBxdjxduLHFrzNB0X/RAofCcoYfj78ei4ffGqn7HXoTHY27OT+xfezs3EnAInWRGLNscRb4rHoLahSJagGcQac1LprqXZV4/SHthwZFSN9ovqQbEvGqrfiDXpp9DZS1FxEvaceo2JkWsY0Lsy+kPEp408I+/6+aMHgNHosJR4fr2ws5fWPtgDQJ97Gf68fzbQBhx/V+n1evn7p70S5xwMwKXI3g0wxwLFHC90XIQRDB8UzdFA8AI3NXt77aAsyv46iz4r4z+eFOO06ItLt9B8Sz4SxKUTa2zcE8r9+/Ib3dz+FVfbhyUmPMbVv9449c6y0+Fq4d+G9lLaU8tjEx5jca/IRQzGrUqWouYj1NespaCxgZ+NOql3VuAIuDIqBaFM0k3pNYljCMGb0nkGUKaqTWtN90GYAGt2Oaq+f5U1Oljc6WNboYIvTg1Llxri2HgDFoDBsUAIfzByJMTzKVYNBqmurqS3ajKdsPbJmB0mVC8iQ5SxVLsAlT6WgMp2ANBNnqWLYOCsDLj0bxXB43/9jwen2M3tOIbs31+GrchPhlQgEQSQOq4I5xUrWwFgGD00gMy0Cg/7Ydsq+t24Jj+ffg0nEMueK94i3do91iPbGr/q58/s7ySvP46UZLzEmeUxXi9Tj0ExAGt2WEo+P5Y2O8MtJgTvkp27VKYyJtHFajJ1zE6KJEIKnVxQxe30FTWUOJmd7uDcbhi88eAO5RxooNvTBdcqvyZ06E6EoeGur2fHlXDatk9R60ogyVJMU58AWoWCLNmOLtWFLjMOWlIA1tRd6a/vkDK6pd7FsRQUFm+twlTmxO1X04b0CHiFxGAXSrscUayQ1PZIBObHkDojHbA5N0F1uD/PmL6F0506aa2twOVpwBFZhCAi8RpXImESi4xIwWCwYTWZMJgs2awQD+o1mSM7YHhOALaAGqHZVU+GsCL0cFZQ7y9lUu4kt9Vv4wyl/4NKcS7tazB6JpgA0ugVBKdnp8rKiKdTZL290UOYNpeiL0usYF2VjfLSd8dE2htqtGFrJ7lVaupm1b/ya83xL9zu+xTwCOfZWojKHk5jRH4O+dQunDAYp+OY7Nv1YQZPLhtMficrB6wgmxYFN34xd38i4K8eSOKZ9wjk4nH7yVldQsrORxmoXvgYfOkcA8z4hfIJIHBYFva6UQPkXmIJuADyKiaDRgttUhV8nsQYMmD0Ck6/1Tt5nUAmk2IjNzmLg0PGMGzEdWxuSsncEftUf6uAdoQ6+zFFGUXPR3o6+2lV9UBrGWHMsqbZUZvafycX9Lu4SuU8ENAWg0am0BIIUuLzscnvZ6fJQ4PJS4Ap99qihv7kEo57xUaHO/pRoOwNsZpQjLM4um/cMI5c+RkDoWdTnGpJSxyICPoLuRnLPvgWj6ehjsUhVxVtfh7OiAmdVLc7aJpz1Tsp3+yhpykQQ5LxLBRlnTD+m36KteF1+tu5sYPO2OsoL6tFt+Z6gJ4+AMYH+M6/mlPGjSIyPbnXdIxjw43Y5cbqbaXE1Ud9czebNKyjfvoVgST3WlvB1isQdp8eWmUrGoGFk5wwnO2Ugkbbo45c/6KWgsYAKZwWVzkoqnZV7R/OVjkpq3DVI9u9vUmwppNnTSLWnkmxLJtWWSoothRR7Csm2ZCz64894pqEpAI1O4Nr1u5hXd1BOoP2YGhvBOQlRTIi2k2Uxtdkbx+/3ser9Ozhl5/usST6N1MtfICmuY4KNeZo9rP9gLuvyFRQR5KwrokmbPLlD7tUaxRt38vnTf8XnriQmbTRX/fE3mCNaD+7WVsqri8hbNY9dW9bgLCrHXONHFw6rHVAkclgyGYOGkTvsNAb0zm3VZfKwMjcXc9t3t1Hc8lPmM5PORLItmWRbMim2lP3ek23JJFuTsRqOr10abUNTABodzvSV29jocB/xOgVINxvJtpoZaDfT32bGrlMQhMI86ITArAgsioJFp2Cq3YLho5tIdxSyZPAtjL/kSXS69ndgc1TWsvaDhWzaYiMgTWRGbee0284iMjOz3e91KBa8/gWrZ7+GEAZGnXcjk646q0P80Z3uFtZv+JGiXZspWLMC427HXoXgMan4Ey1E9U4js/8wRg2bQkZy9iHr2lCzgdvn345E8psxvyErOosUWwoxppgType+J6MpAI0uwx1Uqfb5qfL6KfH42OnyUuD2ssPpYafLi/9wf4NSUrl4yk/f7ckQ3w/isinzxuEzJxLZZyiRWcMwWA69GexwNGzfwZqPl7NtdxISQb+4rYw8qw9xE8+GTopl31Rdz8d/+RcN5asx2dO57KGHSc5qH3fVtuD1uFm3eQmbN+VRVbADf3kd5iYVEV6sdllVSI4gNjOTvgNHMCZ3KnFRSfxQ+gO/Wfwbok3R/OeM/9A7svcR7qTRFWgKQKNb4lclRW4vXlVFBVQZWih2qyoeVeIJqmQveYIUfwNRyf2hvhBZs4MfNjSzsmr/0A8mXRBFhP+eJSAgNtJARk4/+k65gMTcSYg9HjFSUr1iGau/2kpBTQY6/AzqVcTwi0YTOaTz3AzVoMqit75izbdvIVUPGcNmcNH9szAY29899Whpbq4nf8MidmzJp6ZwF7KiGUs4lZNE4oqADRkNyOGp/Pv0f5NsO7qQGhqdh6YANE4IpKry9XNPs23pYoZNmcrAwb1pLt5Gc2UpzuYWQn/PCiggAwFqquqoaDEAArsxQN+sJAzRI6gtiaDS1QejcDEkp5bcy6di7dV5o1dHYzNL3v+a7cvm43NXYrSmcsasuxhwymFTYnQ5VbWl/PjdxxR9Og+A4Lhe3HnXP7XF2m6OthNY44SgbNtmti1dzPhLr2TC5Ve1ycbsKlrPrgUfUrB2HRu31RKU3wFGhC4aj/SwNM/DshVfougsKHozeoMVvdGC3mTFYLZiNNswWW3YY6PIGNyX7FGDUI5x81blrhrmv/YulTsWgfSiNyYxZNrVTL/5cnSHcFvtTkSaIqldtBpzRCQzZt1Bv7ETulokjeOg+//FaZy0BAN+CvJXsHv9GtwtzXgcDupKizGYLYw5/+I2LzBaM4cx5MZhDAF8jTUs+2AOhdsK8XmaMFrt6PUWgkEVv8dJwOcm4HPjbqlGbXCjqh6QPznor5sDQrEQEdeXtAFDGHjqGHoP63fEzVbVRbXMffltqgoWg/QSlTSYsRfNZOiUkYhW9jp0R6SUzHvp37iamrjq8adJyjr0wrBGz6AtGcHMwGJCaYL0wEdSykfDSd7/A9gJJXS5Wkp5kA9gOI/wK8AQQpbZm6SUy4QQscD7QGa4/MzWksJrnHw011Szdu7XbFz4He7mJkw2G7boWCwREaTmDGT4meditByb+6AxOoHJs67haJw6Az4f7pYW6srqKFi1geKNG2is3M6WHzay5Yf3EDobUYn9SB80lEGTxpGak7FXIdSW1DLnpXf2jvhj0oYy/ebryRg84Jjk70o2LpzHjhVLmXT1jVrnf4JwxDUAERpm2aSUDiGEAfgRuBt4DrhPSrlICHET0EdK+Ugr5V8HfpBSvhLOCmYNp5d8CqiXUv5VCPEAECOl/O3hZNHWAE5sqgoL2L78R1Z//TnBYIC+o8Yy9PQzycwdidJJ3jhtRVVVyrYWsWnxSkq3rKe5eidSDa2QKrpILFFp6A1Gmqq3hjr+lCGcfvP19B46sIslPzaCAT+v3vMLTBYL1z757E+L6Ro9gmNeA5AhDeEIfzWEXxLoT2hmADAPmAPspwCEEJHAJOCGcF0+wBc+fSEwJfz5dWAhcFgFoHFi8tRLL7KkxYNODaILBtANHM3AobnUxcWx3WDFVV5F38Q4LMb2jaR5PCiKQvqgLNIHZQFXoKoqhet2sHXJSip2bMLZUIU74CY2bRBTr7uKzNye2fHvoWpXAc01VZz5i7u1zv8Eok1rAOF8wPlANvC8lDJPCLERuAD4HLgcaG1bZhZQA7wWNhnlA3dLKZ1A0p6k8FLKCiFEqwlUhRCzgFkAGRkZR9M2jW5MicfHq6U1zK9rYXu/cQhVRe7TsSyE0FChxg811YhtFcS6WkgLeMgyKIyKjmDagBz6xscd4g6di6Io9B3Rn74j+ne1KB1CxY5tAPQeNqKLJdFoT47KDTRsz/+UUKL3APAsEAd8AdwlpYw74PrRwHJgYlhpPAM0SykfEUI0Simj97m2QUp52Hi2mgnoxMCrqoxetplaX4BTY+ycEWXh2vQkjIqCy+vB6fbQ4PZQ3uKgormZCoeL3W4vhb4gpYqRGlskangncKyrhZHSy7TkeM4f2J8Em+aO2N5U7NzGp0/+CbM9ghv/8YK2u7cH0i5uoGHb/ULgLCnl08CMcOU5wLmtFCkFSqWUeeHvHwEPhD9XCSFSwqP/FKD6aGTR6Jl4VZVbNxZR4wvw0uBMLkiM3u98hMVKhMVKMnAoo4nT62XJzl0sKipmhcvP4sh4vmuBB/O20tvRwFiT4KzevTi9XxZmXfdaO+hp1BYX8fGff4/ZHsFF9z+sdf4nGG3xAkoA/OHO3wJMB54UQiRKKauFEArwMCGPoP2QUlYKIUqEEP2llNuA04HN4dNfANcDfw2/f94+TdLojkgp+aSqgUd2llHvD/LH7NSDOv+2YjOZmDF4IDMGh1SEy+1i3obNzC2rZJWq4yNbIh9WODGUrKa/u4lTIy2cl9OXUSlJWgd2FFTu3M5nf3sMg9HEzEf+TGRCq1ZajR5MW7yAhhFapNURiuP1gZTyT0KIu4Hbw5d9AvxOSimFEKnAK1LKc8LlhxNyAzUCu4AbpZQNQog44AMgAygGLpdS1h9OFs0E1PNwBVV+v6OMvCYHO1xeRkRYub9PMtPiIjvsnuXV1Xy9aQsLa5tYZ7BRGxWyTNo9LnKDbqbERXLBwBx6R598KQDbirOxgdfvux2d0chlD/6JuF7a+ltPRgsFodElXL52Jz80ODArgj9lp3F1ahy6ThyFS1VlfUEBX+/YxZIWL1si4nBZQpm+khyNjBR+Tk+O5+z+2cRFdE2ilO6G3+Phw8ceorZkNzMf/QvJfft1tUgax4kWCkKj0whKyUaHm4V1LfzQ4KC/zcyCMf2PmOylIxCKQm6/fuT2C3ViPq+XH7ZsZU5xOXk+ybyoBGa3CH6Tt53klnqyVR/ZVhPTouycNiIXk+XkWlTetXolC/73Ek3VVZx372+1zv8ER5sBaLQrr5bW8LfCShoCQQBi9DoWjB1AsunglIvdgWangzlbtrOwuoFl0kC5NWSaSmms5cI1izGhEhcZSe/M3gzIHUF63+wek2P3aAj4/fzw9musnv0Fcb0ymHrDLHoPHd7VYmm0E9oMQKPD2eJw8+COMgBeGNSbRKOeEZE2rLru22FG2uycN2wI+UtXUxPQI6Qk2+fiF4oTb1w0tQ2NlDc7KN+4hWUbt6AEAyRE2Bk8dAjjp00/phSU3ZEf33uD1bO/IHvMKZx792/QG7qnwtZoXzQFoNFurG8JZQO7MS2ei5MOu6Wj2/BDUQl3bt5NpcXOgICDJwf1YVzG/olY/H4f29evY/umjewuLqbK4aQqbxULly4nMyWJM86/kJT0nr1Impk7kvyvPkVvNGqd/0mEpgA02oUdTg9fVDcCsLD+8HmBuwtrKmu4Zkcler2BP8WauHVYbqtuogaDkcGjxjB4VChRjMftJm/Bd6xauYpdVbW8+Mp/SbCamHH2OfQbNryTW9E+BLxeAPqNPaWLJdHoTLQ1AI3jYrfby11bislrcmJRBOckRHNHRiID7d178bTJ7eG0Rfk0KwY+H9qb3JSkY6qnaOtm5n75BeUtLhCCSEUyZepURpw2pUftOfjoiUeoKyvh1uf+i6Jtnjvh0NYANNoVKSUNgSD/La0lr8nJlSmxPJiVQoKxZ5gPrl+YR7Upkr/Fm4+58wfIHDCIWQMGUVNZwexPPqKwsoYvvl/Ed/O+47RTJzJ+xtndXhFsW/YDu9evYeIV12qd/0mGNgPQOGoaXT7GzFtPS4QOhGBMpI3PRmZ3qn//8eALqmQsWseEgJNPZpzarnW3NDfzzccfsq1wN6qiYEPlvIsvYWDu8Ha9T3sR8Pt56bbriU5O4fJHnsBwgixqa+zPoWYA3dc9Q6Nbsq2yhdGPf4d/WRVKlYcLE6P5YHjfHtP5AxgUgVENYggG273uiMhIrrjxZn7z4O8YkpmBK6jy/ief8vrzz+Fxu9v9fsfDrjUree3en+NuaWbizGu1zv8kRDMBaRxESb2L297Op7TBTVa8jcy40M7ZXbVO1pY07r3ulr5J/GFQ7y7Z4HU8CCFICngplh0nt9ls4bIbbmJqZQXv/vdlCmvq+PtfnuCcM89ixMT2nXUcC9vzlvD1M08Rl5bOhfc9TO8eunitcXxoMwCNg9hR3cLGsmYaXX5WFzfyyZoyVhTVI4G7ZvRD6kMdZ2FFCy2+9h9FdwZJQtIgOn78E5ecwh0P/Z7Tx49BlZLP587jpb8/haO5qcPvfSB+r4dNi+bz9oP38uU//kJy3xyu+OOTZI8Z3+myaHQPtDUAjYOQUrJkZx1frCvjo/xSVAnZiXY+/eUEIswGlpc18suP11Jf7kSx6bnx9GweGt+nR+2QvXrejywUFnZPyUWv75yJcEtjI+++8iLlDje6YIDpUyZxyukzOvSe1UW7WP/dt1QV7qRmdyFBv5+4XhkMnnw6uTPOwWju3t5aGu2DFgxOo1VWFtXz97nbKGt04w9IAqqKL6DiD0o8gSB7/jyMeoX5v5pMeuxPydj/L383//p2G74WP/ZEK09eNIRzsxK6qCVtp6C+kan5O+gd8PLD2Z1vjlm1eBHfzptHQKcn0WLk6lt/QVRsbIfc64VZ1+BqaqTXoCEkZfUja8Ro0gcP6/aeSRrti6YANFrl3vfX8umasv2OxVgNjO0TS/+kCMxGHdkJdkZnxhJrMx5U3hMI8uu5W/h6WQnSr9KvfyxPnDkQnSKodftQJZzZOw5dNwoHce6cJazVmfliYCqj0lK6RAa308m7r7xIcX0TihpgyinjmXTO+e1+n1fuvJmm6iqu+OOT9BowuN3r1+gZaApAYz88/iC761wU1jp5dUkhZQ1uyhr391IZ3TuGd2eNx9CGzruoycWszzewbUst4oA/qczsGJ67ZBhDY+3t2YRj4ottBcwqb+HioIMXpnf9Yuy6vGV89fXX+BU98UYdV90yi9jEY9+XcCAv/fJGTFYrV/35HxiMpnarV6NnoW0E09hLVbOHqU8vxHWEBdzqFi9qGwcImVFW5l43ju+K6/hgQzk2o54os56F22so2tHAeU8twhBtJDsjmplDUzi9dxwyKEmNMGHUd87mI5/Px++3FEFELI+MGdYp9zwSueNOYUDuCN7/78vsqq7l3889x8SRw5l20aXtYqZR9DriMzK1zl+jVdqSEcwMLAZMhBTGR1LKR4UQuYTSQNqBIuBqKeVBQWCEEEVACxAEAnu0kBDiD8CtQE340gellN8cThZtBtA+VLd4OP3vi2jxBPYeu2REGmcPTUGvE0SaDcTZjKTHWtEpx9cJqarK59uq+WxLJWsK62mudcM+f3ISEEYFvVmHyRK6b06inXP6J3JudiImw/EpB4c/yKKyBhYU1rGqsIZd1W5Esw+DAZ47ty9njx1wXPW3J1vWrubTTz/FJ3RE6+DaWbcRl3TsswE1GOSVO28hKasvF973cDtKqtHTOGYTkAgNQ2xSSocQwgD8CNwNPAfcJ6VcJIS4CegjpXyklfJFwGgpZe0Bx/8AOMLJ5duEpgDaTmWTh8/XluHyBYm1GclKsDEsLZooayhUg8sXYGVRA6/+WMii7TWM6xPL+z/v+EBglU4P/15VTGWzB3QKjQ4vTS4/zU4fDqcfp9uPdAcRADpBdIKVWyZncUn/JJq8AVz+IL5AkGBQhvSIBL8qKXd5KWx0U9LoprzRTXWDm4YmLz6HD6GGb64I7DEmrFEGqne1AHDXmEiK653sqPUSkGA1KNiNCpFmHZFmA9FWIzF2M5mJUUwf0fG5APx+Hx++9irbyyowySB3//YBrLa2mc6klAQDAXR6PS21NSx84xV2rFjK+b/6HTnjJnao3Brdm3ZZAxBCWAkpgNuAeUBUOA9wOjBHSjmolTJFaAqg03n4sw28tbz4oOOn9Yvn7CEpzBicRE2Ll+teXYFeEbw/6xQy4qyt1NS5qFKyps7Bp1srWVZQR8GWuqOvRIDebiAq0kSvOCtDkiKZ1ieO0zJi9pqb/vbpcl5dUYVbhqygMTofRkXiUQVeVcErdUj2n/18/YtRDM5MPu42toXvv/ycxflrGJaVySXX3XDE66uLdvHhnx7E43RgstrwupwIRWHSVTcw6ryLNa+fk5zjWgMQQuiAfCAbeF5KmSeE2AhcAHwOXA6kH6K4BOYKISTwopTypX3O3SGEuA5YBfxaStnQyr1nAbMAMjJ6dsz1zuT2qdlICVsrW8jf/dPPWlLv4sFPN/DgpxsAiLYaeOeW7tH5AyhCMCo+glGnRsCp/Vhc0sBTC3aQEGnCatRh1OvQ6wSKIiD0D50iSDAbyY61MiDWRmaUBf0RFq7vv3g8vzzLS35BBcP6JBNt2z8MgqqqNDjc/OfrFby8zsXgGJWBGYkd2PL9mXreBeStWMGWHTsJBPzo9YcPsudoqMPjdACQPfYUkrP6kT5kGHFph/pvqaFx9DOAaOBT4E4gADwLxAFfAHdJKeNaKZMqpSwXQiQSmjXcKaVcLIRIAmoJKYjHgBQp5U2Hu782Azg2pJRc8sJS1hQ3UvDnc9hW2cKi7TVYjTrOG5ZCnF1bIGyN6oYWJj81H4lg5SNnEWHt3N/pu88+4ce160mxWbj11/cf1vwkpeS1e39BQ0UZ1z75LImZWZ0oqUZ3p12CwUkpG4GFwFlSyq1SyhlSylHAu0DBIcqUh9+rCSmPseHvVVLKoJRSBV7ec1yj/XB4Azz17VbOfuYH1hQ30i/Rjk4RDEqN5LYpfbl+QqbW+R8GRVHQKwKP1PNV3uZOv//0iy4hPSaSCqebl/7+FOphgtcJIZjxi7sAWPPtVwQD/s4SU6MHc0QFIIRICI/8EUJYgOnA1vCIHiGEAjxMyCPowLI2IUTEns/ADGBj+Pu+O3Au3nNc4/gpb3TzwcoSbnptJf+3sIBoq4HfnT2At24Z19Wi9Sjio2x8fc9kInR+np5fhMPl6XQZbrzzHnrHRlHp9PDi009xuBl7SnYOqTkD2bhgLks+eLsTpdToqbRlDSAFeD28DqAAH0gpvxJC3C2EuD18zSfAaxAy+QCvSCnPAZKAT8MLUHrgHSnlt+EyTwkhhhMyARUBP2+fJp3cVLd4mP6PRXt9/K8Zn8HjFw3tYql6LhkJ0YxLM/NdcZAF63Zx/ikH+Tl0KIqicP0dd/PGv5+lqL6RxbO/ZvI55+FoqGfnyuU011ThamrE2dRIQ3kpTdVVAEQndc5itUbP5ogKQEq5HhjRyvFngGdaOV4OnBP+vAvIPUS91x6tsBpHxqAo+23w2lTeM/LzdmcCwZAf6aShfbrk/oqiMPOmm3nq6b+zauF8atbkUbxhHVKq6PR6LFHRWCOjSOqbw9DTzyJ90FBSc7rP/gaN7ou2E/gE44bXVuz3PbdXNP6g2qZwDhqtkx1vYWGZi+pmN1GdnOtYSklNTQ1fffIxAP66GhpczYy7+HIGTJxCbFovzcVT45jRFMAJxozByawr/SnW/P+WFvHW8t0MTIlkYnY8t03uu3czmEbbcPtDM6oDXUU7mqqqKj799FMqKysRQmAN+pl5z330HpKrdfoa7YKmAE4wbp+azW2T+1Ln9FHa4GJXjZOdNQ6+31LNfxYVoFcE953Zv6vF7PZUN7QQlJIIs5F15U50KMTaO08BbN26lY8++giTycTZZ5/NoEGDiIiI6LT7a5wcaArgBKLO4eWhTzcSadETYTYgZWhnrS+o0uj2YdAJpvTv/vH6u5Ky2iYe+3A583b7kICCJICeYfoKvv12Nn379mXAgEPb110uF16vl5iYmKO+9/r168nPz6exsZGmpiYiIyOZNWsWdnvXR1HVODHRFMAJxHPf7+TbTZX7HYsw6zHqFPonR/DStQPITY/uGuG6Me8uXMdLPxTi9qvU+vQEURgRq2IWAWobm8lUGkhRmlm5spSVK1diMpnIysqif//+xMTE4HQ62bRpE4WFhbhcLgD0ej1JSUnk5uYyYsQIDIb9zW6NjY3Mnj2bkpIS3G73fu6dw4YNIyoqimHDhmmdv0aHouUD6KYUNRXx8Y6Pmbd7HopQmNxrMlPTpzIiaQQGpXUbvjcQ5PznfqSswY0z7AkUadYzIDmS/skR5CRH0C/RTk5SRKvJXY6FdRWFvL72W1x+J+6AB3fQjTfowRv04Fc96BUjZp0Vi86GVW/FarBhN9iwG+1EmexEmSOY3GcAvWO6bmYy6vefU+fTkxMRICVCzy+mD+KUQT95/ASDQRoaGqitrSU/P5+CggJUVT2oHkVRSExMxGq1Ul5ejsfz076B6OhocnJySElJIT8/n9LSUgB0Oh3BfTZ4DRkyhMsuu6wDW6txMqIlhOlB/FD6A79e9Gv8QT+npJ6CRLKiYgU+1UdOTA53jriTzMhM0uxpGHQHKwO3L8jmimY2lzextbKFbeFXi/en8M8jMqI5d2gKl4zsdczK4Pm8T/nPlj/wU7hNkKoeIY2hF0YkAaTwIBUPQhzcaQJIKbCofRkaM47z+5/GOTljMOnbR0G1hfP+8hm7WyD/j+dhNBx5UiylpKioiB07duByuTAajfTr14/s7Oz9FmcbGhpYvnw527Zto7Gxcb867HY7Z5xxBrm5uXz77bcsX76ciy66iOHDh7dz6zQ0NAXQY/ho+0c8vvxxcmJyeHbasyTbQht6XH4X84vn89jyx3AHQpm7FKGQYkuhV0Qv0iPSSY9Ip5e9F/GWeOIscaTYUjDqQh2plJLKZg87qhxsKGvimw0VbCpvJtZm5OnLhzFtwNHFnfcHgox4cywyaObeYY9z4aBRRFtsGHStx++XUuLye6h1NdPgaqHe00yD20GNs4klJavZ3JSHT1cSulg1Eq/vT27CaCalj2NgfCZxtigiTaHkMQLBb+e+zLb6bVzc/2wmZQ4lK/bY4+b/9rV5vL/Nx69OieGuCycccz2Hw+/3s3btWqqqqhgzZgxJSUkEAgHWrFnD0qVLaWho4MYbb6R3794dcn+NkxtNAXRzpJT8e+2/eWn9S0xMm8jfJ/8dm8F20HV17jqKmosoc5RR0lJCcXMxpS2llLSU0ODdP5iqQJBgTSDNnkaaPY2sqCz6x/ZnQOwAEiwJbK1sYdabqyhv9LDsgWkkRppZX1HCGyvXUFDbQHKUiez4WAYlJTMirTfJEVF76y5uqOWsD2eiGBpZf8MqFHH8+wy211Tw3oaFLCnLo9yzEYxVbS8ctGMT6aRY+pAT059RKQOZlDmE5MjDe84sXLuTO97fgEfqefP64ZwysOOjZwaDQZYsWUJ+fj5NTU2kpqZy2mmnMXDgwA6/t8bJiaYAujFSSv6R/w/+t+l/XNrvUh4a/9Ah7fyHw+FzUOoopd5dT427hnJHOWWOsr2vCmfF3mtjzbGk2dPYtPFUmhp6k55cS0D6qahKIOQbEAT2H80LnQeL2Y05Ng+/fSEACkYmpo2jd2Rv+kX3o39sf1LtqdgMNgyK4Zj91YOqZG1ZKfOLllPlrMHhc+AN+glKCVLFFXDROzKDnNhM1lVvo7B5J7Xe3XhEGUIJhH9XBSWQQISSTmZkNuN6Dea0jKHkJvdBURTeX7yBh74pxKyovHTtKCZ0QucPsHDhQhYuXEhWVhbjxo0jJydH8+vX6FA0BdCNWVSyiDu+vwOA7OhsfEEf3qAXKSWT0ifxyPhH2mWE7fA52N6wna31W9nWsI1KZyVlpf3YuG0gOoMDRUj6pfn41bQRTMkaRHFjA2vLi9lSVcWuukbKGtzUtqg4la3o4+cc1b0VFKJMUSycubBDs2oFggFWle9kWckGNtZspailgHr/bgLKPvmIVDMGGYWzZjTRjrG8c9sk+qbGd5hMe5BSsnDhQhYtWsSgQYOYOXNmh99TQwM0BdCt2VS3iT/n/ZkIYwQWnQWjzohRZ+SznZ9h0Vv48Wc/7rXldzeklFQ4K9hYu5Ft9dsodZTS4G3AH/TjV/0E1AB+1c/Oxp2oUuXM3mfy9JQ2J4FrNypbGvm+YD35FZvYVLeOsuASAGafvZheiUfvs38szJ49m7y8PHJzczn//PPR6zUvbI3O4bgygml0LIPjBvP2OQeH751TNIczep/RbTt/CMWhT7WnkmpPZUbmjENe5w64OfOjM5mzew6/avkVqRGpnSglJEdEc9XwSVw6ZBw//+TnlLkhMZjYaZ1/TU0NeXl5AEybNk3r/DW6BVqEsG5MZmQmJS0lXS1Gu2DRW7hjRMjMdeHnF/Lpjk87XYbq5moue/cy8t35TDRO5Ourv+6U+65cuZIXXngBgIEDB2KzHby4r6HRFWjDkG5Kk7eJSmcluYmtRtPukczsP5PCpkLe2fIOv1/6e55c8ST9Y/tzesbpXJB9AdGm6A65b1l9Gfd9fR/bA9vxCz/XJ1/Pr8/8dacsvJaXl/P11yFFc/PNN5OeruXo1eg+HHENQAhhBhYDJkIK4yMp5aNCiFxCWcDshBK6XC2lPCj4vBCiCGgh5FYS2GOHEkLEAu8DmeHyM1tLCr8vJ8IagCpVSlpKcAfcGBUjBp0Bk86EXtEj+KlD+u3i37KyaiXvnvsuA2JPrNjuVc4qfvfD79hYt3HvngaAofFDeXbqs8Rb27Yg2+CqpN5VTp/Y4QghqGwppKh+HcVN26hoKcaotzMyeSIfr/mRb5tDeYieGPoEF4y8oEPa1RrNzc28//77lJWVYbVaue+++zp0EVxDozWOZw3AC0yTUjqEEAbgRyHEbOA54D4p5SIhxE3A/cAjh6hjqpSy9oBjDwDzpZR/FUI8EP7+27Y2qCdR2FTIu1vfZUvdFrY1bNuv0zscf5rwpxOu8wdIsiXx6lmvAlDuKOeTHZ/w5a4v2VC7gQs+v4ClVy7de225o5xl5ctYX7OewuZCKp2VNHobUYMuvDKkMCN1Arcq8bc2ltk2e+/HZ0f/k6mDp3do2w7EbrczatQoysrKiI2N1Tp/jW7FUXkBCSGswI/AbcA8IEpKKYUQ6cAcKeVB+fLCM4DRByoAIcQ2YIqUsiKcH3ihlPKwcYp76gzg8eWP8/629xmZOJKBcQPpH9OfSGMkPjXk7ukL+gioYd91Qs8jPSKdSb0mdaXYnc7Nc25mReUKMiMzafG10OBtQJX7h4/QCR12g50UpY4ck4oxciwlLcUkWuJItafRK7IPmdGDSYnKptFVyp+X/YHVTaE/vduiIdM4mdxht5OW1jmbrj744AM2b95MRkYGl156KVFRUUcupKHRzhyXF1A4H3A+kA08L6XME0JsBC4APgcuBw5l3JTAXCGEBF6UUr4UPp4kpawACCuBxKNqUQ/CpDNh1pl5/ezXu1qUbs3dI+/m6m+upqi5CINiIMmaRFZUFoPjBjMqaRTDE4djNVipbNzImlUXUqfvyzVT3jxkfUkRffnv+fP4sfB9fI4KZPV36PWz2bxlDqvXDCKrz00MHHh+h47KS0pCi/j9+vU7KCKohkZXc7QzgGjgU+BOIAA8C8QBXwB3SSnjWimTKqUsD3fw84A7pZSLhRCNUsrofa5rkFIe5JMnhJgFzALIyMgYtXv37qNoXvfgbyv/xhub32DF1Suw6Ds3pWBPo9JZiVlnJtoc3ep5KVU+XXQqpkAV2UPfpH/S0cXuqa3dyPoNz+H3L0av9+H3n8JZZ77VDpK3TnFxMd9//z1FRUXo9XqGDh3K2LFjSUlJ6bB7amgcSLvsA5BSNgohFgJnSSmfBmaEK88Bzj1EmfLwe7UQ4lNgLKFF5SohRMo+JqDqQ5R/CXgJQiago5G3K/EFfXxf/D0f7/iY5RXLiTZF77fIq9E6e4LfHYqCqoVEqVWUmUZy7lF2/gDx8UMYNvQB8vI2odNVEB01/BglbRsZGRnccMMNVFVVsWLFCtatW8eaNWvIyMhg3LhxDBgwAN0hAuhpaHQ0R1QAQogEwB/u/C3AdOBJIURiuFNXgIcJeQQdWNYGKFLKlvDnGcCfwqe/AK4H/hp+/7xdWtTFVDoreWPzG3xZ8CWN3kZSbancPvx2Ls6+GLO+c3PKnohYrVl4VbDrWg8tfSRKS1ewfsMsDEYX8fEPMzz3xnaWsHWSkpI4//zzmT59OmvWrGHFihV8+OGHREREMGbMGEaNGqXtD9DodNriBjoMeJ1QZDAF+EBK+SchxN3A7eHLPgF+F14QTgVekVKeI4TIImQygpCyeUdK+US43jjgAyADKAYul1LWH06WnrAI/KuFv2Le7nnM6D2DS/tdyvjU8e0Sx0fjJ16ZN55kUc/ZUzeiO4pd0lVVa1i77mqkqqP/gGfpkzm1A6U8PKqqsmPHDvLy8ti1axcA99xzD9HR0V0mk8aJyzGbgKSU64ERrRx/BnimlePlwDnhz7uAVncySSnrgNOPKHkPQyd0pNnT+PuUv3e1KCcscQlnYal/k/XFHzCizzVtKtPYWMiatTcgpcLAAa+RmXnQ/4VORVEUoqKi9pp/oqKitBmARqej7QRuR/yqn/U16+kT3efIF2scM5NzfsnSpW+yo+SdNikAt7uOZcuvRFG8ZGQ82+Wdf21tLQsXLmTjxo2YzWamT5/O2LFjNS8hjU5HUwDtyL/y/0W5s5yHxj+099jqqtV8U/gNvqCPCGMEdqOdSGMkAEvLl2I32MmMzCQzKpOsqCxyYrTY8EdCygCqBJtvO1LKw/5efr+bhYuuQK+vIy72jwwaeOiAdR1JZWUlW7dupaioiKKiIgwGA6eddhoTJkzAYtE8wzS6Bk0BtBMBNcAbm98AQm6f72x9B1/Qx8rKlVj0FiIMEbT4W/bbBZxoScSgMzC7cPbeDWDD4ofx4hkvYjfau6QdXU0g6GNZwWtU1S9F9dejBp1I1Y2QHhTpQy8DWEQAmw4qOPzWEVUN8v3312A0FmIy3cHIkVd1Uiv2p7CwkDfeeAMpJUlJSUyePJnRo0cTEXH4bGUaGh2NpgDaCb2i57djfsvC0oVEGiMpd5TT7GsObW4aePVe/3+/6sfpc9LibyHZloxBMeAJeChuKWZhyUKeW/Mcv1/6e/4x5R9d26BORkrJ3C3P0Fz2IrE6H1ESnKrAK3UEhQEVI1KJQugs+AxRDM68ntN7XXjYOhcsvB29YS3Iyzh14r2d05BWyM/PR0rJXXfdRWxsbJfJoaFxIJoCaEeuGXQN1ww6vE3aoBiINkfvt9HJrDeTE5NDgycUC2/e7nnM2z2PiakTsRqsHSnyQXg8DhSpYLR0zn2dvkaWbn+OuspPSFCaUYSRQMKNTMy5HZsx+pjNYXl5jwPz8PlO48wZf2lfoY+S0aNHs2nTJubMmcPMmTM1v3+NboOWEawb4Vf9vLLhFV5Z/wo+1cfQ+KG8c+47nXLv+uId5D3zCAnz12HxQEuEgjPGgi8+Em+UBQmIoIoIqhAMogRVCKrIYCD0XQVFBZ0qUXUKQYNC0KAjYND99NmoYLJGEBmZQGRWCi2x7+LHi0WR1Ab0mGPP5Ozcv2I8TqW3YeOrVFc/gcs5kLPO+gSDoesT6ixfvpxvv/2WqKgoTjnlFEaMGIHJZOpqsTROErSMYD0Ag2LgttzbiDXF8nje4wTUAHXuOuIsB0XYaFfW5X2J/+e/Jd0r2T0qBdG7F8GqanTVDVhLakjYoiIFqIpAVULvUicI6gQoCuh0BBXC50DvkZj9Kga/ii4gMfhV9P7QuxIeb5T/nw89P/0BZlRGYqkqomj3A9jiBhOZMRpb+jCUo/SMKSz8lsrKv+BxpzFt2lvdovMHGDduHDExMSxZsoRvv/2WJUuWMGvWLG0dQKNL0WYA3RCX38WDPz7I/OL5ANwx/A5+nvvzdr9PQ00p6977P4xvfI6CIPa/z5MzbHK732dfpN9PfX05hdu+RHUsxuzQ4QmU4TXU449wI/fpr4UX9E0mTL4o9MZo9IZIDKZoDNY4zLZ0FEx4GgtwO0rw+qtxigbcMY14vTa250/H5lHQCYGCQCcERiEwKAJVpyOAwBcM4ldVVDWIKiV7/i9IQoGujMEgZglmnQ6j0YDRZMJoMmGyWjFaLFgjIxk0/QzMMdF7ZVZVlfqiIkrXrKFq925cbg9SDRIXF8eQadOIGzCAgoIC3nzzTUaNGsV5552neX1pdDhaUvgehJSSwuZCXtv4Gp/t/AyjYiTv6jz0yrFP2OqLd1CwbA4Nq/OQOwqxlzUS2xQEoCrRQOY/nyVz1JR2asGxoapBXFXbaCleiaN6A07nLjxqBT5DE0G9D2mSyNaiaaigaxYIt4VyJYnKgmHYmvWohLIQqRKCAvyKgl+nQ1FV9IEABlVFDyEloSggwtGahEAvBH5FwS0lXiAgBH6dDtlK5FCD348hGEQVgoBOR2CffL/6QAAJBMPHEhwO0mJiWOv3AzA1PZ3JN9/cvj+khsYBaCagHkCDp4FXN77K/OL5e3MBD44bzMPjHz6qzt/ZVMvO5XOoXrmEwKatRBdUE90cxA6YFahKMtIwIAVHVh8SJ09n0qTLukWiEkXRYU8ZhD3loLQSAKgeD4G6ary15TgaNwMSa0J/rL0GY4js+OTuUkoCHg+ehgY8DQ1UFRRQtGEDHlXi0+vRCYFeUYiLiSGtf3/SRo3CEhuLlJLKDRtYv3AR2zwe1vp8WL1e3EYjzo8+wtGvH/ZJJ1fuB43ugTYD6Ea8sO4F/m/t/wFwLsMYts1LdEENluom7E1+aqIE/rR4pF4HBj3SYkbRGxB6PdYaB2nLCgBQBXtt7TWxOhr7JqIbMoCEURPoP+5MoiISuqqJJzX+6mp8u3bh2bqVlm/n4F67FoDe77yNdeTIrhVO44RGmwH0AH7W/2eUtpQS9crnnL98NRAyz7gSozD4mogIKIiiBpRgeFHVp6ILSnTqTx0+hD43/OZ6+p55KQPT+nVRazT2EGxqovLxJ2j+8su9x4y9e5P0uweIuugidFqWMI0uQlMA3YgYcwwPKudSvPwTANLnfcPA9LbFFVL9fny7d1P6y9vxFxcT87c3cM9bj/vB32EZOrQjxdY4AjXPPEvzl18Sd+st2CZMwJSdjS4+Xlv81ehyut7wqwGE7MuNH31Eyc9/galfP7IXL8Lexs4fQDEYMGdnkz13Dkm//z26uFjca9ZQdPlMCq+4AtXl6kDpNQ6FVFVUpxOA+F/8Atspp6BPSNA6f41ugTYD6GKklDgWLqT2uX/j2bwZY3Zfer/5BrrjiAsfe9WVxF51Jc4VK6h4+BE869azY9rp9P12Nnot3nyHEnQ48WxYj6+4hJb53+FasRLp8aBPSkJYO3dXt4bGkdAWgTsQ1e0GRUFpZcen6vXS9PnnNLz5Jt4dOzGkpxP/i58Tdf75CGP7bl4qvvc+nLO/piU2m5TH/kja1OHdwuvnRMOzbTu7r7pq74hfn5pCxLTTMWVnYzv1VIy90rpYQo2TlWNeBBZCmAnl8DWFr/9ISvmoECKXUBpIO1AEXC2lbD5EHTpgFVAmpTwvfOwPwK1ATfiyB6WU3xxlu7ol0uej5Be/wLl0GSgKhrQ0TH37YsruGxoFqpKmL7/Av7sY06CBpPzlL0Seew5KO3f8ENqYVLWxBDvQZEpm5YeN2N75mH45BkbeMg1LXGS73/NkRPV6qXz0UVSnk7Rnn8E8YACGtDSEFvdHoxvTFhOQF5gmpXQIIQzAj0KI2cBzwH1SykVCiJuA+4FHDlHH3cAW4MDe5p/h5PInFJ5t23EuXYYuPp6YK67Au6sA384CHD/+CIEAAIbeGaS/8gq2iRM61B5cPnsJ9pL1+M+9gYn330r0Oz+wY5OPtYVxFD84mytfvKLD7n0yUfHII7jXriX1yb8SOaNrcg5oaBwtbUkJKQFH+Ksh/JJAf0IzA4B5wBxaUQBCiF7AucATwK+OX+TuTaChgbJ770WfkEDmRx9iSErae06qKuwxuSlKu3f8qqqy5cGn8azOR7FaUYUO89blqDozvW68DGtyLGN+dSFjgPn3/Y+tjgwWPfoek//4s3aV42RDqiotc+cBoE9K7mJpNDTaTpsWgcMmnHwgG3heSpknhNgIXAB8DlwOpB+i+L+A3wCtRb26QwhxHSHz0K+llA2t3HsWMAsgIyOjLeJ2GoG6OhSzGWG1IoRA+nyU3XU3gepqer/15n6dP4BoR7u7Ggyy8+VPaf5hCUp0HJZ+WTi//w7b9mUo9iRkrcToa8E96kz6PXg7UYOy9it/6sOX0fS7z9hYmUz//B0kj9L2C7QFKSXB2lq8Bbtwr1mNe9Mm1OYWpMcDQPENN5B4//3E3XxTF0uqoXFk2qQApJRBYLgQIhr4VAgxBLgJeFYI8XvgC8B3YDkhxHlAtZQyXwgx5YDTLwCPEZpNPAb8PVzngfd+CXgJQovAbWpVOyKlJNjYSKCqCn9FBYHKSvyVVXg2b8b5ww8ACKMRXUwMQq/HX1ZG6t+ewjJsWLvLoqoqu96dS8uK1ajrV2Kt2IpZZ0IX9MJ8sAGOUecy6o2nUHTKYdMlmqLtnPGbabz957Us+e9KzstKxhSjRaY8HM7ly6l4+BH8paWhA0JgzMxEHxeHdexYZCCAe/169AnxXSuohkYbOWovICHEo4BzX9u9ECIHeEtKOfaAa/8CXEsouKKZ0BrAJ1LKaw64LhP4Sko55HD37ggvoKDDib+sjEBlBf6KSvxVlQQqKvFXVoY7+8q9o7u96HTokxKJOH06hqREAg0NBBsaCTY0YDvtVGKv6pjUg5seexHl7X8B4LXGwdQLGPaXe1H9fhrWbKOloIS0y87EZGt7nPkFD7zB5sZe6IIeIoINuIUdKQRG4cegBDDpVYz6IIpUEUYTwmwkMsZITLKViORoItJisWckobe2FqXtxEH6/WwfNx5dbCyx112HMSsLy9Ah2i5ejR7B8XgBJQB+KWWjEMICTAeeFEIkSimrhRAK8DAhj6D9kFL+DvhduJ4phBaNrwl/T5FSVoQvvRjYeEwtO0qklLTMmYtjwQLcGzbgKyz8yS4PoCjoExMxJCVhGjAA+5Qp6JOTMCSnYEhJRp+cjD4+vku8O7z5K7AAWcuWY4r5qeNRjAYSTh1BwqkjjrrOyX++ht7frGDLd+U4PHqSTC2IYACfD3x+BafPQKNiQwoF6VCRKPhrLbBDAg2hl9xBglLLhU9egCnyxPR1d+atQHW5iLroQmKvu7arxdHQaBfaYgJKAV4PrwMowAdSyq+EEHcLIW4PX/MJ8BqAECIVeEVKec4R6n1KCDGckAmoCGj/gPetEQxSds89ANinTiXy3HMwZWWhT0oOdfAJCQh999wfpzpa8FjjMUS1n6lGURSyzhtP1nnj2yaDy4WjtIbG3bU4Kptw1DqoLGyhxJfCa79aQKyuidjIIJFxJqLTY0ga05+onO61dnO0uPLzKf3lLzH27UvMNYdP+amh0ZM4KTeCFc+ahXfrNvotXtQOUnUeGx59Hv37/8Y5ZCqJN11LrzPHoei6x4aunZ8uYdviIuqa9DgMsUgRniFJlYkj/Az/xdldK+Ax0vT115T/5rcY0tLIfP899DEdH3ZaQ6O90RLC7EPR1dcQbGyk79dftYNUnUcwqLL2xt9iXvktigzgscYjR0wk+bLzSJ5xCko32XSkBlWaSuup21RM3ifbaVZimDBeR+5N07tatKPCt3s3uy66GFN2NhmvvKzZ+zV6LFo4aMC1ejW1//437vx8kh58sKvFOWp0OoVRb/wNT9UD7H7rS4Lz5mFZ+jVNSz6nxhxFcNgEIqZORRU6vIWFGCLt9L/32k4P+6DoFGJ6xxPTO57kUVl8+eBXLFmeSPLIQpKGtz3AXVfT9OVXSI+HXs89q3X+GickJ4UCUH0+Kv/0J5o++hhdfDyJ999HzNUd46nTGZiT4uj/6xvg1zfgqmqg6K2v8H//HeZV3xFYMRsI7dYDaD5nMtEDM7tKVOxJ0Zx7/0Te+9sGvn1+NTP/HIMlIbrL5DkaFKs15CAguoeZTUOjvTkpTEBbBgwEwDRgAJnvvoNisbS3aN0Cb0MLtYtWoDMZqPh8HuaFH+GZcQ0jnn2oq0Vj6/9m8/0yPUZfC73ttQw8ayBpZ03o1rFydl9/A67Vq+m3aCH62NiuFkdD45g5qU1AppwcvNu34926lZJbZyEsFvSxMRh698Y6YgSW3NzQaK+HY4qJIO2i0wEoe+09ACIHZXelSHsZcMPZ6BJWs3ZuM9s9WWz/0k/kB+/SJ0My+PLxxAzrfjuR/aWlGHtnaJ2/xgnLSTED8JeXU/3PfxFsbkK6PahuN4HaWgKVlaEpvl6PZfBgrGNGYxk1CuvIkT3a5htwe9k2ciQBexy5KxcfuUAn46h1sOm9ZezY0ESTiAWpkhAoI3tYNIOunoQ5vnt42lQ+9jgNb79Nvx9/QB+v7e7V6LloXkCtEGxpwb1mDa5V+bhWrcK9YQP4/SAE5mFDibvlFiLPOKPd7tdZFH04D/cjdwEQ+fybpJ1+0HPvNtRsKmHjRyspLBG49VHogh5SjTUMmJhG34tPRWfpmh3GqtvNthGhRO3ZC77HkJLSJXJoaLQHmgJoA6rHg3vdelwrV9L8zTf4du0i4Z67ifv5z3tUCj/V62PTTXejz18IQEBvBQFIidTpUc68jP6P3oXB1n3CN6hBlaJ5a9kydxulLVEEdGZMvkYyIuoZetkYUk7L7VR5fMXFFMw4E4ABWzb3qOevoXEgmgI4SqTPR8Ujj9D0+RdEX3EFyY883G13CB8KV2UdO555k0B5KagSiUCtKCWidB2O9FzGzHuvq0VsFb/Tw9bPVrA9r5IqbwwSwdBejZz64MWdtteh7pVXqH7676S//BL2007rlHtqaHQUmgI4BqSU1PzzX9S99BL2adNI+/vT3caDSEqJVCWqKlF0CorSthFq0B9g45jT8CVlMWbO2x0s5fHjqKhn9u+/olrXi+Hp9Ux86LIOvZ+UEu/WrRTPmoUxrReZ773boffT0OgMTmovoGNFCEHir+5Fn5hI1RNPUHzDjfT6zwsdEg4gGAiy48WPcSxfgfR6ET4PeFwQDCCFgs7RgOJuQVH9KAEfStCHIDSql0JBCoWg0UogKgmZlI4+IR4FlaDXR9DjC0XzVCC4uxCbpxHL+ee3exs6AntKLJe+eA0f/vID1u2OJe7dBQy4cmqH3a/2hReoffY5FJuN5D/+ocPuo6HRHdBmAG2kee5cyu+7H0NqKumvvIyxV692q7t4XQUtP5uOItX9jruj0kDRIRSBao2C6FhUxQBmCzqLBcWgQxESGQwS9AVQmxoR1WUYGsrRex2oQodU9EhFtzc2jy7gxpMxlJHfvNmjEsO765r58Dff4lQiOe/qVNKntH++BYCCc89DBvz0fu01DKmpHXIPDY3ORpsBHCeRM2agfy2Okl/eTtHPriT9xf9gGTy4Xepu3FWFIdz5BxUDihpAINF7mvEPnYhl6BAicgcRO3oI1vi2RQJVgyoBv4reqNvPPKSqao/q+PdgiYvkwgdP5b0/fcFH//kfkS/66NUrneSsbIxWG3qzBUNEBNa0XkT2ycIUEbF34VYGg0ivl6Dbjc/pwJiUjMHUes4Ey5DBOBYuQp+Y2JnN09DoErQZwFHiLSig+NZbURubSPzN/URdfDHKITqTo8HnCWA0h/RxwOWh4O3ZNH/6GZbda9EFQ8nWJAJX2mAy//UU8UN7Tkyd9kKqkrnPvc3GpaHFa6FK5CHWPhRVYgoGQUJQEQQUgRpWfIoqSbFF0Hf4KPpdcAnRWX33lmv+dg5l99xDxv/+h238uI5vlIZGJ6AtArcj/qpqSm+/Hc/GjeiTkoj/5S+JvuRihMFw5MJHSdAfoGF9AU35G2h86w2s1Tvwn38Tw/52f7vfq7vQWirLFV8sZNlHbxHwVgKg6CP55b+eoWbrZnxOBwG3G39TE+6aahw11ThbmvFIFUXRoTcaMRiNGIwmDAYDjcW7KWmsxaMPmcUipCAtNZ0+E04jc+p0dk+dhiExMRT+OSGh09uvodHeaAqgnZFS4srLo+Zfz+BeuxZDRgbxt91G5LnnoBiN7X4/1ednw9jTUI1WBn37KabYnrtT+VAUrt/Jkvc+pbpwJQA6gw2d3kgw4CPgrUExRNN//Az6jBxBar90oo4jqJwaDFL2/XwKvp9L6a4d1Kh+VEVBSIkxEGRYSTWjX3gJ6+juu4lOQ6OtHLMCEEKYgcWAidCawUdSykeFELmE0kDaCWX0ulpK2XyIOnTAKqBMSnle+Fgs8D6QGS4/U0rZcDhZupMC2IOUEseiRdT86xm8W7eii4kh7pZbiL3xBkQ72tpX3ft3bLNfQbn7Mfrf1rGukJ1JMBBg1VcLWD37G1yNOwCBPa4fZnsMXpeDgM+LzmDEFpPIGTdfTVKfjtmR662tofDLz9m1cjlb6irJ6TeI8x9/qkPupaHR2RzPIrAXmCaldAghDMCPQojZwHOEcvwuEkLcBNwPPHKIOu4GthBKCr+HB4D5Usq/CiEeCH//bdub1D0QQhAxZQr2SZNwLltG/euvU/23v+FcsoTUp//WLoHE6reXYZr7Ft6s4Qz7+SXtIHXX43G5+e7lt9i58nuC/haEzk7v4ecw5ZpLiE9P7nR5TPEJ5Fx3I0UeByysZMCFF3e6DBoanc0RFYAMTREc4a+G8EsC/QnNDADmAXNoRQEIIXoB5wJPAL/a59SFwJTw59eBhfRABbAHoSjYJ07ENmECjR9+SNXjT1D0syvp/dabGI7To2T3P17EpPpI//Pve6QHz4E0VNbxxm/uI+CtwWjLIveMa5h4xRkYze1vOmsrUkq+euZJduQtZfwlV5A9um05kjU0ejJt6k2EEDohxFqgGpgnpcwDNgIXhC+5HEg/RPF/Ab8B1AOOJ0kpKwDC7632kkKIWUKIVUKIVTU1NW0Rt0sRQhAzcyYZ//sfgZoayu6+B+nzHVedwepKvJZYoocPbCcpu5YF//uEgLeGcZfezR2vPMPU68/t0s5fDQb5/rX/sCNvKTnjJjLximu12D8aJwVtUgBSyqCUcjjQCxgrhBgC3ATcLoTIByKAg3o5IcR5QLWUMv9YBZRSviSlHC2lHJ3QgzwyrCNHkPrnJ3CvWUPlE38+5nqKP1+EZcsyZFpWO0rXNZTvKGb2/71D0bq5mGzpnDrzDEQbQ1h0FFJKvv/fS6yd8zWjz7+E8+7psZNQDY2j5qg2gkkpG4UQC4GzpJRPAzMAhBA5hMw8BzIRuEAIcQ5gBiKFEG9JKa8BqoQQKVLKCiFECqHZxQlF5Nln49m8mbqXX8E8eBAxM2e2uWxjQTk7//B3LKu+xW+NZcCLT3egpB3L6jk/8sPb/yXgDc3g9KZ4zrvnV0co1fEEAwG+f/U/rJ//LaPOu5jJ19zU1SJpaHQqR1QAQogEwB/u/C3AdOBJIUSilLJaCKEADxPyCNoPKeXvgN+F65lCaNH4mvDpL4Drgb+G3z8//uZ0PxLuuQfP5i1UPvY41jFjMPU58gaudQ8+g/L5/7CqPjyjziTnLw9gTe05s599aWloYcGrT6PobPQbdxlDpk0gc1h2l69lBHw+vvn30+zIW8rYiy7n1Cuu7VJ5NDS6grb8L0wBFggh1gMrCa0BfAVcKYTYDmwFyoHXAIQQqUKIb9pQ71+BM4QQO4Azwt9POIROR/If/wh+Py1z5h7x+l0P/R/GT/6DPrYv/mmPYhx+FY6KYCdI2jHMfv4tIMCEK27mgl/dQNbwnC7v/KWUfP704+zIW8qU627htCuvb1eXXQ2NnoK2EawDkUEVb1ETFb+7D8/6peiT0oi5+lYMaSn4y6oJNDXjLyrElJNDzM/OoexXf8SdPw8laRDq9F+h80iMARW9EHgUgTIuhdTJaZijW0/k4nMHqFxQglQEUdnRKHoFT6WTpuIWgmuqkBmRKJawE5cklCAm/B76HuocQ8L/dHxP2GkZUDHWN6PzB1nhzqPZ34ga8KEG/aiqHzXoR6p+pBpASj9IPyCJTBjCjf96HH03yaeQ//VnLHzjFaZcdyujzr2wq8XR0OhwtJ3Ax4lUJcEWH8EGD4F6D2qLH9UXRPqDSG8Q6VORviCqX0V6gwSbvQSbfMigivO7h5HOtnkwWSeeT6/n/oTOGurkXXVuip9dg9kTQC8EqpQ4TXoMA2Ox9YnE3+zDlGClelEpSpmDKF3nLKourv2GWn81is6Aojeg6Izo9EZ0BgM6vRG9wYjeaMQWE8sZt8zEZO0e2cd8Hjcv33EzZpuNm/71kubto3FSoCmAY8Bf48KxpBzvzkYCDR4IHvxbCYOCMCoIo27vSzEq6CKM6KJNGHtFYMqOBr1CsK4Z15pN+IrKMPVNw9grBX2vZBzf59GyYAlqUyPpLzyBYj44uFzQF6B8SQXN62vQVTixH3BeSolLp+BLjyR2aBwtRc0IVUUfb8UWb8GeZgdFIAQhzxtFIBTCx346LoQInw+5tAY8Hnb+dwERlRH4VA++ASo5V01Dbzn+AHidjaoGmf3vf7B1ySJmPvoX0gcN7WqRNDQ6BU0BHCX+ahfVz61B+lXMA2MxJFrRxZjRx5rRxZjQRRoRBl2XuDGqQZXa9TU072pCZzEQdPqJH5FAdHb7Jqqp+HETLV/uxioiqLNW0+eW04hM7Zlhkqt27WTuS89RXVhA7hlnM/2W27taJA2NTkPLB3CUeAubkH6VmEv7YRvT+aEJDoeiU0gckUTiiKQOqV/1B9n24jzsJVakqhKcYSH3jEs75F6dQXXRLt7/wwOYrFbOufM+Bkyc3NUiaWh0CzQFcAjM/WIQRoWmebuRUmIblYTQnfieIo2FZZS/vIpINZZaSyU5d52BNTa6q8U6ZoIBP/Nf/Q86o5Gr//Iv7DHHH5tJQ+NEQVMAh0Afaybh1mE0fllA4yc7cfxYTuyVAzCm2LpatA7B7/Wx5fU52HaasGDHmesj96rLevQiaTDgZ97Lz1O+bTNn/fJerfPX0DgATQEcBmN6BAm35eLZXEfDZzupeWEdcdcPwtw3uqtFa1eK5q7A+V0ZsUo8TmMzidcNIian/XIedzZSSnatXsGSD96mpmgX4y+5gsGTT+9qsTQ0uh2aAjgCQggsg+Mx9oqg5pUN1L6yAdv4FKLOzEQx9+yfr6WshqIXfyDGl4BJWAieYiLn/HO6PD7P8dBSX8vXzzxF2dbNRMQlcOF9D5M9RovsqaHRGj27B+tEdFEmEu8YTvOc3TiWlePZVEfU+X2xDInrkWaSou9XEfi2lgiiac5ood+N0zDYuoev/rGgBoNsW/YDS95/E1dzM2fMupMhU6aj6HRdLZqGRrdFUwBHgWLSE31BXyzDE2j8ZCf1b2/B1C+a6AuzMcRbulq8NrP19XlYNhtQhYrlil5kjszpapGOGTUYZOvSxSz/+D0aKsqI65XBpb/7I2kDBnW1aBoa3R5tH8AxIoMS5/JymubthqAk9or+WIbEd7VYR6Tk+zXIOc20GBrJvm86puieuah9YMefkJHJ+MuupN+YU7S4PhoaB6DtA2hnhE5gn5iGZUg8ta9tpPGrXZgHd29zUMXqzfi+rSGAn773Teuxnb/f6+HjP/+esq2bSejdhwt+/SDZo8drHb+GxlGiKYDjRBdlwpQVjWNZOdKvIozd0+bcuKuMpvd2YRQmkm4dgTk6oqtFOiY8Tgdf/uMvlG3bwpm33cPgSdO0jl9D4xjRFMBxIqXEs70BU58olG7Y+Qf9AXa+twjjBokJC4azYonOTu1qsY6JhspyPnvqMRorKzjrtns0104NjeNEUwDHiWtlFYFaNxFTupfffPnSjdT9uBNzrQmbYqdJV0fKDaOI7X+o1M3dm50rlzP7+X+g6HRc9vBjWiA3DY12oC0ZwczAYsAUvv4jKeWjQohcQlnA7EARcLWUsrktZcPn/gDcCuyJk/yglLItiWS6DY68Cho/34mpXzTWDorLc7R46prZ8fx8YlzxRMooWoyN6EZGMPDCC1B6aCiLZR+/y9IP3iYpqx8X/Op3RCb0zIB0GhrdjbbMALzANCmlQwhhAH4UQswGniOU4nGREOIm4H7gkbaUlVIuD5//Zzi3cI8iUO+heW4RrrU1mPvHEHvVAEQnxeE/FGogSOHnS2GFm0hiaExqJPumKfTuobZ+CJnXVn35CUs/eJsBEydz5m33oDcYulosDY0ThiMqABnyE3WEvxrCLwn0JzS6B5gHzOEABXCYsj0O1RvAs6Ue56oqvAWNoFOImJpO5PTeXdr5q/4Am5//FmO5glWJoEV6MF2UxJAJU7pMpvagatdOlrz/JoVr88k55TSt89fQ6ADatAYghNAB+UA28LyUMk8IsRG4gFAy98uBVo3LrZXd5/QdQojrgFXAr6WUDa2UnwXMAsjIyGhru44ZGZQE6t0EatwEalz4a9z4Sx34q5wgQRdjIvL0DGxjktFFdX1SlK2vziO6MoomYx2eEZBzwdnoemhHKaWkZNN68j77kOINazHZbEy+5iZGnXuR5umjodEBHNVGMCFENPApcCcQAJ4F4oAvgLuklHFtKSul3CiESAJqCc0IHgNSpJQ3He7+HbERTPpVPDsb8Gytx1fmwF/pgoC697xiN2BIsWHMiMSUFYWpT1S3ipWz7TdfEzQEGfj4+d16D8LhUNUgBfkrWPHZh1Tu3I41KppR515E7hnnYLJau1o8DY0eT7tsBJNSNgohFgJnhW33M8KV5wDntrUssFFKWbWPcC8DXx2NLMeLVCXN84tx/FCG9AURJh3GXnbs41MwpNjQJ1gwJFhRLN3XUUoGJVYRQX10bY/s/D1OBz+88z+2/LAQv9dDVFIy02/5JYMnT0dvNHa1eBoaJzxt8QJKAPzhDtwCTAeeFEIkSimrhRAK8DAhj6A2lQ2fS5FSVoQvvRjY2D5NOjKqJ0DFX1ciPQEsQ+OxjUnGlBWF0PcsM8PuRavQC4Ep8cAMwT2Dr5/9G8Ub1jLwtKn0GT6afmNP0YK3aWh0Im0Z3qYAr4dt+QrwgZTyKyHE3UKIPYlVPwFeAxBCpAKvSCnPOVTZcJmnhBDDCZmAioCft1ObjkjLD2VITwBj70jirh7YWbdtV5qLq2iZV0KkjKXvxad1tThHTdHafIrW5jP+0iuZOPPqrhZHQ+OkpC1eQOuBEa0cfwZ4ppXj5cA5hysbPnft0QrbXkRMTKVlfjHBJi/+SieG5J4VE6exoIzaFzcQJWJx9w9giOw5kUgBfG4Xc1/+N7GpvRh30eVdLY6GxklLz7J5tBOK1UDCL3ORAZWqf6+l8YuCkJdPD8Bd30zVi2tQ0GGamUL/m6Z3tUhHzdYli2mpreGMWXdotn4NjS6k+65wdjCmjEiS7h5J0zeFOJaW41hajj7JimVIPMZUO4pFhzDoECYd+hgzwtA9dGXRx8uIUKIITjWTNKp/V4tzTGzPW0J0cgppAwZ3tSgaGic1J60CANBFGIm9oj8RU9Px7mjAtaGWlvnFB1+ogD7BijHFhiHVjiHFhiHFhs7eeaNXd30zBa8vIqLSTrOoZ8AZ53favdsTj8NByab1jDrv4h7puaShcSJxUiuAPRgSrRgSrdgnpqF6AgRq3Ki+INIXRHqD+Ktc+CuceAubcK2t2VtOiTBiTLVhSPlJKejjzIh2irnjrKxn1+dLkSUeInwxRCvRNFhqybptMkoP3RhVkJ+HGgzSb+wpXS2KhsZJj6YADkAx6zGmHzp+TtDpx1/hDL8c+MudeHaUghreUKeEcgToY83oYszoo03ook3ookIvfbylTRvJir7OQy5yEKNE4ZF6nDEO4k/PYejYnufxsy87VizFHhdPct+em4ZSQ+NEQVMAR4nOZkCXHY05O3rvMRlQ8VeHZgmBOjeBeg/Beg+ebQ2oLb79ygujgiHFjikrCnO/aAzpESiGg33fA4sakahYZ6aTNqLfCWEu8XncFK1bzbDpZ50Q7dHQ6OloCqAdEHoFY6odY+rBG7JkQCXY5CXY5CPQ4MFf5sBX2kLLohJaFpTgV324Ay2oBFGMeqQARQoilBgcahNJPTBhu8fpoHDNKpqqq3A2NuBqbMDjbMHR0EDQ7ydn7MSuFlFDQwNNAXQ4Qq+gj7Ogj7NgIgpGhfIGqJ4Aru11rHzhXUw6K4rQoQR0KEKHQFChFGLPTWbX6pUoej2KogMkUpWYrFasUdGY7XaEotBQUY7H4SDg9xL0+Qn4fQT9fgJ+P0G/n2DAjyUykujEZKKSkrHHxoXraz+CAT+Fa/LZ8sMCClavIOj3A2Cy2bBGxWCxRxCVmETmsBGkDuiZm+80NE40jioYXFfTEcHgugPBQIAdeUtYO/cb6kqL8ThaOvR+Or2eyIQkopOSiU5OJSoxicjEJKISkrDHxGIwm9EbTa2aaVzNTTRWluNuacFRX0dTdSWNlRWUbN6Ax9GCNSqa/hNOY+DEKST07qP5+WtodAPaJRicRseg0+sZMHEyAyZOBsDv8+Ksr8fncaMGAgQDAdRgACEUEOB1uXA1N+JxOFCDQaSqkpozEIPZhN5gRGcwojcY0O156fQ4mxppqqoMddhVFeFXJWXbNuNzuw8WSgiMZjMGkxmD2YwaVPG5nHicjv0uU3R6ohKT6DNiNAMnTqb3sBFaPB8NjR6CpgC6IQajiejklHatM9qcTHRS8kHHpZR4nI6wcqjC1dyI3+PB7/Xg97jxeTz4PR4URcFgsRKdlExsWi8sEZHYomM6xJykoaHROWgK4CRHCIHFHoHFHkFy335dLY6GhkYn0jN3E2loaGhoHDeaAtDQ0NA4SdEUgIaGhsZJiqYANDQ0NE5SjqgAhBBmIcQKIcQ6IcQmIcQfw8dzhRDLhBAbhBBfCiEi21o2fC5WCDFPCLEj/B7Tvk3T0NDQ0DgcbZkBeIFpUspcYDhwlhBiPPAK8ICUcijwKXD/UZQFeACYL6XsB8wPf9fQ0NDQ6CSOqABkiD27fwzhlwT6A4vDx+cBlx5FWYALgdfDn18HLjoG+TU0NDQ0jpE2rQEIIXRCiLVANTBPSpkHbAQuCF9yOZB+FGUBkqSUFQDh98RDlJ8lhFglhFhVU1PT2iUaGhoaGsfAUcUCEkJEEzL33AkEgGeBOOAL4C4pZVxbykopNwohGqWU0fucb5BSHnYdQAhRA+xus8DtTzxQ24X372i09vVstPb1bDqyfb2llAkHHjyqncBSykYhxELgLCnl08AMACFEDnBuW8sSmj1UCSFSpJQVQogUQjOEI93/oAZ0JkKIVa0FVDpR0NrXs9Ha17Ppiva1xQsoITx6RwhhAaYDW4UQieFjCvAw8J+2lg2f/gK4Pvz5euDz42mIhoaGhsbR0ZY1gBRggRBiPbCSkB3/K+BKIcR2Qh16OfAagBAiVQjxzRHKAvwVOEMIsQM4I/xdQ0NDQ6OTOKIJSEq5HhjRyvFngGdaOV4OnHO4suFzdcDpRylvV/NSVwvQwWjt69lo7evZdHr7elRCGA0NDQ2N9kMLBaGhoaFxkqIpAA0NDY2TlJNeAQgh3hdCrA2/isKb1hBCXL3P8bVCCFUIMfwoymcKIdz7nDvIS6oz6Kj2hc/9TgixUwixTQhxZqc1qg3ytbV94WvvDLdhkxDiqfCxE+L5ha89qH3h4z3++Qkh/iCEKNvnunPCx0+I53eo9oXPHf/zk1Jqr/AL+Dvw+1aODwV2HU15IBPY2NVt6sD2DQLWASagD1AA6Hpa+4CpwHeAKfw98UR6fodp34ny/P4A3NfK8RPl+R2qfe3y/LSUkGGEEAKYCUxr5fSVwLvHUb7L6YD2XQi8J6X0AoVCiJ3AWGBZuwl9FBxH+24D/hpuB1LKI25I7Ao6oH0nyvPrEXRA+9rl+Z30JqB9OA2oklLuaOXcFRz5AbVWvo8QYo0QYpEQ4rT2EvQYae/2pQEl+5wvDR/rKo61fTnAaUKIvPBzGrPPuRPh+R2qfSfK8wO4QwixXgjxqtg/rPyJ8Pyg9fa1y/M7KWYAQojvgORWTj0kpdyzA7lVLSyEGAe4pJQbj3CbA8tXABlSyjohxCjgMyHEYCll89G34PB0UftEK9d0iE9xB7dPD8QA44ExwAdCiCxOnOd3qPadKM/vBeAxQrI/RsjMchMnzvM7VPva5fmdFApASjn9cOeFEHrgEmBUK6d/xpHNIweVD0/N9ky784UQBYRGY6uOSvg20BXtIzTi2DcCbC9CO8LbnQ5uXynwiQwZVlcIIVQgXkpZw4nx/FptHyfI85NSVu1Tz8vAV+HjJ8T/v0O1j/Z6fl29MNIdXoQC1C1q5bgS/qGzjrY8kEB4UQbIAsqA2BOofYPZfxFqF120iHg87QN+Afwp/DmH0LRanCjP7zDtO1GeX8o+n+8lZBc/Yf7/HaZ97fL8tDWAEIfSwpOAUinlrv9v745REIiBMAq/uYoH8Sq2gpXX8AxiLeIBvMYu6jG8gYxFmi22UEy1874yEMgwhJ9ASKaDEXGMiOmrfXPz18AYEQNwBbaZ+eq45l90ry8zH8AFeAI3YJeZ766r/t4/9Z2AVUTcgTOwybbDltK/2foW1L9DtG9pR9qNp/1k7hL6N1tfr/75FIQkFeUJQJKKMgAkqSgDQJKKMgAkqSgDQJKKMgAkqSgDQJKK+gCBRwcLdRXCwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  201388.0 people (VAP of 159890.0 ) and  89090.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 50\n",
      "working on tract 100\n",
      "working on tract 150\n",
      "working on tract 200\n",
      "working on tract 250\n",
      "working on tract 300\n",
      "working on tract 350\n",
      "working on tract 400\n",
      "working on tract 450\n",
      "working on tract 500\n",
      "working on tract 550\n",
      "working on tract 600\n",
      "working on tract 650\n",
      "working on tract 700\n",
      "working on tract 750\n",
      "working on tract 800\n",
      "working on tract 850\n",
      "working on tract 900\n",
      "working on tract 950\n",
      "working on tract 1000\n",
      "working on tract 1050\n",
      "working on tract 1100\n",
      "working on tract 1150\n",
      "working on tract 1200\n",
      "working on tract 1250\n",
      "working on tract 1300\n",
      "working on tract 1350\n",
      "working on tract 1400\n",
      "working on tract 1450\n",
      "working on tract 1500\n",
      "working on tract 1550\n",
      "working on tract 1600\n",
      "working on tract 1650\n",
      "working on tract 1700\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%50 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.8339285714285715 560\n",
      "100 0.5274872620005363 3729\n",
      "200 0.8095238095238095 1008\n",
      "300 0.796448087431694 732\n",
      "400 0.7294117647058823 1360\n",
      "500 0.8340336134453782 476\n",
      "600 0.4325068870523416 363\n",
      "700 0.4166666666666667 1644\n",
      "800 0.8230366492146597 1910\n",
      "900 0.8416050686378036 1894\n",
      "1000 0.7330689444783405 3278\n",
      "1100 0.8642779587404995 921\n",
      "1200 0.6529801324503312 3020\n",
      "1300 0.7081058378832423 2381\n",
      "1400 0.7807848443843031 739\n",
      "1500 0.86 100\n",
      "1600 0.42574257425742573 101\n",
      "1700 0.2948061448427213 1367\n",
      "1800 0.8036363636363636 275\n",
      "1900 0.0 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 10.894879133875014 10.552697921462013\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  6910835 2996167 0.6182777037206635\n",
      "compare to Census 6910840 Leip has Trump + Biden = 2996186.0 0.6182777037206635\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 6910840\n",
    "atlasTrump = 1852475.\n",
    "atlasBiden = 1143711.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 6910835 6910835.0\n",
      "state Trumpers before, after slice opn are 1852460 1852460.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 84 grids for 9 districts\n",
      "starting grid no 0 at x = -90.1208845952381, y = 35.194840375 \n",
      "starting grid no 5 at x = -89.74167178571429, y = 35.618673125 \n",
      "starting grid no 10 at x = -89.36245897619048, y = 36.042505874999996 \n",
      "starting grid no 15 at x = -88.98324616666666, y = 36.466338625 \n",
      "starting grid no 20 at x = -88.22482054761905, y = 35.194840375 \n",
      "starting grid no 25 at x = -87.84560773809524, y = 35.618673125 \n",
      "starting grid no 30 at x = -87.46639492857142, y = 36.042505874999996 \n",
      "starting grid no 35 at x = -87.08718211904761, y = 36.466338625 \n",
      "starting grid no 40 at x = -86.3287565, y = 35.194840375 \n",
      "starting grid no 45 at x = -85.94954369047619, y = 35.618673125 \n",
      "starting grid no 50 at x = -85.57033088095237, y = 36.042505874999996 \n",
      "starting grid no 55 at x = -85.19111807142856, y = 36.466338625 \n",
      "starting grid no 60 at x = -84.43269245238093, y = 35.194840375 \n",
      "starting grid no 65 at x = -84.05347964285714, y = 35.618673125 \n",
      "starting grid no 70 at x = -83.67426683333333, y = 36.04250587500001 \n",
      "starting grid no 75 at x = -83.29505402380951, y = 36.46633862499999 \n",
      "starting grid no 80 at x = -82.5366284047619, y = 35.194840375 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 558421.7017558936 5415924.939738656 77.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 9   #TN congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.447062281985338e-06 1.708610750001721e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1701 tracts\n",
      "I am working on tract number 20 of 1701 tracts\n",
      "I am working on tract number 40 of 1701 tracts\n",
      "I am working on tract number 60 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 74 1 449738.02870469744 1.9939\n",
      "8 yoyos for tract,wedge,wedgePop,r= 74 1 87084.13684265473 0.9969\n",
      "9 yoyos for tract,wedge,wedgePop,r= 74 1 449738.02870469465 1.9272\n",
      "10 yoyos for tract,wedge,wedgePop,r= 74 1 178681.39511859138 1.4621\n",
      "11 yoyos for tract,wedge,wedgePop,r= 74 1 407636.20020154794 1.6947\n",
      "12 yoyos for tract,wedge,wedgePop,r= 74 1 265884.5824105267 1.5784\n",
      "12 yoyos for tract,wedge,wedgePop,r= 74 1 341313.4976184704 1.6365\n",
      "13 yoyos for tract,wedge,wedgePop,r= 74 1 374267.02587010263 1.6656\n",
      "I am working on tract number 80 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 99 3.0 0 90.0 1.0904 193607.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 99 4.0 0 90.0 1.0834 193607.4\n",
      "I am working on tract number 100 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 117 3.0 3 36.0 0.9349 555836.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 117 4.0 3 36.0 0.6918 555836.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 119 3.0 3 36.0 0.8304 740262.7\n",
      "I am working on tract number 120 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 131\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 139 5.0 0 144.0 2.9408 493588.6\n",
      "I am working on tract number 140 of 1701 tracts\n",
      "I am working on tract number 160 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 160\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 164 4.0 1 90.0 1.6515 463062.6\n",
      "we have 2 non-opposing shorted wedges for tract no 168\n",
      "I am working on tract number 180 of 1701 tracts\n",
      "I am working on tract number 200 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 5.0 0 90.0 0.4264 225068.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 6.0 0 90.0 0.3775 225068.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 7.0 0 90.0 0.3342 225068.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 214 8.0 0 90.0 0.2958 225068.8\n",
      "I am working on tract number 220 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 228\n",
      "we have 2 non-opposing shorted wedges for tract no 234\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 236 2.0 0 90.0 0.3389 55591.2\n",
      "we have 2 non-opposing shorted wedges for tract no 236\n",
      "I am working on tract number 240 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 256\n",
      "I am working on tract number 260 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 263 3.0 3 36.0 0.5811 235046.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 271 2.0 2 90.0 0.4359 203921.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 271 3.0 2 90.0 0.4164 203921.4\n",
      "we have 2 non-opposing shorted wedges for tract no 276\n",
      "I am working on tract number 280 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 283 3.0 3 36.0 0.6235 258300.5\n",
      "I am working on tract number 300 of 1701 tracts\n",
      "I am working on tract number 320 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 327\n",
      "I am working on tract number 340 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 348\n",
      "we have 2 non-opposing shorted wedges for tract no 349\n",
      "7 yoyos for tract,wedge,wedgePop,r= 350 1 1133712.888403123 2.1183\n",
      "I am working on tract number 360 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 369\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 376 2.0 1 90.0 0.376 43280.9\n",
      "we have 2 non-opposing shorted wedges for tract no 376\n",
      "I am working on tract number 380 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 382\n",
      "we have 2 non-opposing shorted wedges for tract no 385\n",
      "we have 2 non-opposing shorted wedges for tract no 386\n",
      "we have 2 non-opposing shorted wedges for tract no 393\n",
      "I am working on tract number 400 of 1701 tracts\n",
      "I am working on tract number 420 of 1701 tracts\n",
      "I am working on tract number 440 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 440\n",
      "we have 2 non-opposing shorted wedges for tract no 441\n",
      "7 yoyos for tract,wedge,wedgePop,r= 447 3 276406.0044348713 3.7629\n",
      "8 yoyos for tract,wedge,wedgePop,r= 447 3 82244.41296027869 1.8815\n",
      "9 yoyos for tract,wedge,wedgePop,r= 447 3 276434.3449370313 3.783\n",
      "10 yoyos for tract,wedge,wedgePop,r= 447 3 114646.39330989687 2.8322\n",
      "11 yoyos for tract,wedge,wedgePop,r= 447 3 268847.9165564794 3.3076\n",
      "12 yoyos for tract,wedge,wedgePop,r= 447 3 160624.72668573278 3.0699\n",
      "13 yoyos for tract,wedge,wedgePop,r= 447 3 231902.6001440834 3.1887\n",
      "13 yoyos for tract,wedge,wedgePop,r= 447 3 202240.95664869278 3.1293\n",
      "14 yoyos for tract,wedge,wedgePop,r= 447 3 181413.5310447546 3.0996\n",
      "we have 2 OPPOSING shorted wedges for tract no 459\n",
      "I am working on tract number 460 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 472 3 362573.7679859714 0.8652\n",
      "8 yoyos for tract,wedge,wedgePop,r= 472 3 122434.74986537936 0.4326\n",
      "9 yoyos for tract,wedge,wedgePop,r= 472 3 362597.03450066526 0.8657\n",
      "10 yoyos for tract,wedge,wedgePop,r= 472 3 287177.37926606135 0.6492\n",
      "10 yoyos for tract,wedge,wedgePop,r= 472 3 312924.9029199236 0.7575\n",
      "I am working on tract number 480 of 1701 tracts\n",
      "I am working on tract number 500 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 518 4.0 1 90.0 0.7088 194174.0\n",
      "I am working on tract number 520 of 1701 tracts\n",
      "I am working on tract number 540 of 1701 tracts\n",
      "I am working on tract number 560 of 1701 tracts\n",
      "I am working on tract number 580 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 584\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 595 5.0 0 90.0 1.1415 307395.0\n",
      "we have 2 non-opposing shorted wedges for tract no 596\n",
      "we have 2 non-opposing shorted wedges for tract no 597\n",
      "I am working on tract number 600 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 602\n",
      "we have 2 non-opposing shorted wedges for tract no 605\n",
      "we have 2 non-opposing shorted wedges for tract no 618\n",
      "I am working on tract number 620 of 1701 tracts\n",
      "I am working on tract number 640 of 1701 tracts\n",
      "I am working on tract number 660 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 672\n",
      "I am working on tract number 680 of 1701 tracts\n",
      "I am working on tract number 700 of 1701 tracts\n",
      "I am working on tract number 720 of 1701 tracts\n",
      "I am working on tract number 740 of 1701 tracts\n",
      "I am working on tract number 760 of 1701 tracts\n",
      "I am working on tract number 780 of 1701 tracts\n",
      "I am working on tract number 800 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 803\n",
      "I am working on tract number 820 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 834\n",
      "we have 2 non-opposing shorted wedges for tract no 839\n",
      "I am working on tract number 840 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 843 3 491807.8439647707 2.1906\n",
      "8 yoyos for tract,wedge,wedgePop,r= 843 3 18948.55417895969 1.0953\n",
      "9 yoyos for tract,wedge,wedgePop,r= 843 3 491431.66180083953 2.1818\n",
      "10 yoyos for tract,wedge,wedgePop,r= 843 3 149581.46657058463 1.6386\n",
      "10 yoyos for tract,wedge,wedgePop,r= 843 3 357335.18948698475 1.9102\n",
      "11 yoyos for tract,wedge,wedgePop,r= 843 3 483813.4121409566 2.046\n",
      "11 yoyos for tract,wedge,wedgePop,r= 843 3 459588.1239887507 1.9781\n",
      "11 yoyos for tract,wedge,wedgePop,r= 843 3 407149.7667850026 1.9441\n",
      "12 yoyos for tract,wedge,wedgePop,r= 843 3 380051.16343470605 1.9271\n",
      "13 yoyos for tract,wedge,wedgePop,r= 843 3 392973.60500774824 1.9356\n",
      "we have 2 OPPOSING shorted wedges for tract no 850\n",
      "we have 2 OPPOSING shorted wedges for tract no 855\n",
      "I am working on tract number 860 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 872 2.0 3 90.0 0.6354 202281.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 872 3.0 3 90.0 0.6108 202281.7\n",
      "we have 2 non-opposing shorted wedges for tract no 878\n",
      "I am working on tract number 880 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 885\n",
      "we have 2 OPPOSING shorted wedges for tract no 887\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 889 2.0 3 90.0 1.0815 197570.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 889 3.0 3 90.0 1.0584 197570.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 889 4.0 3 90.0 1.0357 197570.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 895 4.0 3 36.0 1.53 401347.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 895 5.0 3 36.0 1.4762 401347.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 895 6.0 3 36.0 1.4243 401347.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 895 3 401347.3000503117 1.4346\n",
      "we have 2 non-opposing shorted wedges for tract no 898\n",
      "I am working on tract number 900 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 912\n",
      "I am working on tract number 920 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 922\n",
      "we have 2 non-opposing shorted wedges for tract no 936\n",
      "I am working on tract number 940 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 945\n",
      "we have 2 non-opposing shorted wedges for tract no 951\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 953 2.0 2 90.0 0.8772 332716.0\n",
      "I am working on tract number 960 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 960\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 962 3.0 1 36.0 0.9531 526982.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 967 3.0 1 60.8 0.9825 886816.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 967 4.0 1 60.8 0.7992 886816.8\n",
      "we have 2 non-opposing shorted wedges for tract no 969\n",
      "I am working on tract number 980 of 1701 tracts\n",
      "I am working on tract number 1000 of 1701 tracts\n",
      "I am working on tract number 1020 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1020 6.0 2 90.0 2.4766 224710.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1020 7.0 2 90.0 2.1953 224710.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1020 12.0 2 90.0 2.5424 224710.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1020 13.0 2 90.0 2.2536 224710.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1020 18.0 2 90.0 2.0535 224710.8\n",
      "I am working on tract number 1040 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1040\n",
      "we have 2 non-opposing shorted wedges for tract no 1041\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1048 7.0 3 47.9 4.2621 486586.0\n",
      "I am working on tract number 1060 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1078\n",
      "I am working on tract number 1080 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1090\n",
      "I am working on tract number 1100 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1112\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 8.0 1 43.4 3.779 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 9.0 1 43.4 3.7403 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 10.0 1 43.4 3.702 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 11.0 1 43.4 3.6641 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 12.0 1 43.4 3.6266 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 13.0 1 43.4 3.5894 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 14.0 1 43.4 3.5527 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 15.0 1 43.4 3.5163 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 16.0 1 43.4 3.4803 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 17.0 1 43.4 3.4446 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 18.0 1 43.4 3.4093 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 19.0 1 43.4 3.3744 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 20.0 1 43.4 3.3399 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 21.0 1 43.4 3.3057 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 22.0 1 43.4 3.2718 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 23.0 1 43.4 3.2383 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 24.0 1 43.4 3.2051 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 29.0 1 43.4 3.7281 377890.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 30.0 1 43.4 3.6899 377890.3\n",
      "loop31.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0378, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 31.0 1 43.4 3.6521 377890.3\n",
      "loop32.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0374, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 32.0 1 43.4 3.6147 377890.3\n",
      "loop33.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.037, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 33.0 1 43.4 3.5777 377890.3\n",
      "loop34.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0366, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 34.0 1 43.4 3.5411 377890.3\n",
      "loop35.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0363, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 35.0 1 43.4 3.5048 377890.3\n",
      "loop36.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0359, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 36.0 1 43.4 3.4689 377890.3\n",
      "loop37.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0355, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 37.0 1 43.4 3.4334 377890.3\n",
      "loop38.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0352, 11261.7,0.0002, 372747.4,-0.0125\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 38.0 1 43.4 3.3982 377890.3\n",
      "loop39.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0348, 11261.7,0.0002, 372747.4,-0.0125\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.18141 8.3909 11114.1204 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 3.36341 377890.2597 377890.2597 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.05611 11162.0366 11241.5691 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 3.36798 376027.7211 373126.0804 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1119 39.0 1 43.4 3.3634 377890.3\n",
      "loop40.0, tr1119,wedgePops773372.03, 11114.1, 377890.3, 11241.6, 373126.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0301 \n",
      "   targetWP, latest drx4 are tWP,dr, 372747.4,0.1801, 372747.4,-0.0344, 11261.7,0.0002, 372747.4,-0.0125\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.18141 8.3909 11114.1204 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 3.32897 377890.2597 377890.2597 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.05611 11162.0366 11241.5691 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 3.36798 376027.7211 373126.0804 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1119, giving up w pop 773372.0296571441\n",
      "I am working on tract number 1120 of 1701 tracts\n",
      "I am working on tract number 1140 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1142 6.0 0 90.0 0.9297 292144.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1142 7.0 0 90.0 0.6656 292144.4\n",
      "we have 2 OPPOSING shorted wedges for tract no 1151\n",
      "I am working on tract number 1160 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1167 4.0 1 90.0 0.8367 619467.8\n",
      "we have 2 non-opposing shorted wedges for tract no 1171\n",
      "we have 2 non-opposing shorted wedges for tract no 1174\n",
      "I am working on tract number 1180 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1185\n",
      "we have 2 non-opposing shorted wedges for tract no 1186\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1193 2.0 3 90.0 0.7504 275452.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1193 3.0 3 90.0 0.5645 275452.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1197 2.0 1 90.0 0.8556 355693.2\n",
      "I am working on tract number 1200 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1215 4.0 1 90.0 1.7945 1107126.8\n",
      "we have 2 non-opposing shorted wedges for tract no 1216\n",
      "we have 2 non-opposing shorted wedges for tract no 1217\n",
      "I am working on tract number 1220 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1220\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1230 2.0 2 90.0 1.0286 221852.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1231 3.0 3 90.0 0.9861 379572.2\n",
      "we have 2 non-opposing shorted wedges for tract no 1235\n",
      "I am working on tract number 1240 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1244 5.0 1 43.8 1.4898 453932.1\n",
      "I am working on tract number 1260 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1272\n",
      "I am working on tract number 1280 of 1701 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1295 2.0 1 90.0 0.9614 225944.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 6.0 0 90.0 1.075 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 7.0 0 90.0 1.0517 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 8.0 0 90.0 1.029 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 9.0 0 90.0 1.0068 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 10.0 0 90.0 0.9851 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 11.0 0 90.0 0.9638 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 12.0 0 90.0 0.943 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 13.0 0 90.0 0.9227 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 14.0 0 90.0 0.9028 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 15.0 0 90.0 0.8833 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 16.0 0 90.0 0.8642 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 17.0 0 90.0 0.8456 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 18.0 0 90.0 0.8273 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 19.0 0 90.0 0.8094 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 20.0 0 90.0 0.792 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 21.0 0 90.0 0.7749 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 22.0 0 90.0 0.7582 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 23.0 0 90.0 0.7418 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 24.0 0 90.0 0.7258 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 25.0 0 90.0 0.7101 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 26.0 0 90.0 0.6948 197614.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1297 27.0 0 90.0 0.6798 197614.1\n",
      "loop31.0, tr1297,wedgePops772388.4, 196810.6, 191493.6, 191816.2, 192268.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?3023 \n",
      "   targetWP, latest drx4 are tWP,dr, 191967.6,-0.0048, 191967.6,0.0168, 191967.6,0.001, 191967.6,-0.0012\n",
      "loop32.0, tr1297,wedgePops588256.96, 12679.1, 191493.6, 191816.2, 192268.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?3023 \n",
      "   targetWP, latest drx4 are tWP,dr, 191967.6,-0.2935, 191967.6,0.0168, 191967.6,0.001, 191967.6,-0.0012\n",
      "loop33.0, tr1297,wedgePops763376.98, 187799.2, 191493.6, 191816.2, 192268.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4023 \n",
      "   targetWP, latest drx4 are tWP,dr, 191967.6,0.2302, 191967.6,0.0168, 191967.6,0.001, 191967.6,-0.0012\n",
      "loop34.0, tr1297,wedgePops766261.36, 190683.5, 191493.6, 191816.2, 192268.1, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?4023 \n",
      "   targetWP, latest drx4 are tWP,dr, 191967.6,0.0034, 191967.6,0.0168, 191967.6,0.001, 191967.6,-0.0012\n",
      "I am working on tract number 1300 of 1701 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1310 3 1084185.6513762937 3.2085\n",
      "we have 2 non-opposing shorted wedges for tract no 1319\n",
      "I am working on tract number 1320 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1323\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1324 3.0 0 90.0 1.0603 206449.1\n",
      "I am working on tract number 1340 of 1701 tracts\n",
      "I am working on tract number 1360 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1369\n",
      "I am working on tract number 1380 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1381\n",
      "I am working on tract number 1400 of 1701 tracts\n",
      "I am working on tract number 1420 of 1701 tracts\n",
      "I am working on tract number 1440 of 1701 tracts\n",
      "I am working on tract number 1460 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1470\n",
      "I am working on tract number 1480 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1480\n",
      "I am working on tract number 1500 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1502\n",
      "we have 2 non-opposing shorted wedges for tract no 1504\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1511 12.0 3 36.0 1.9662 1028878.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1513 3 1064298.2501013367 2.0563\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1514 3 1079423.1101871019 2.3864\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1515 4.0 3 36.0 1.669 991650.0\n",
      "I am working on tract number 1520 of 1701 tracts\n",
      "I am working on tract number 1540 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1552\n",
      "I am working on tract number 1560 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1560\n",
      "we have 2 non-opposing shorted wedges for tract no 1570\n",
      "I am working on tract number 1580 of 1701 tracts\n",
      "I am working on tract number 1600 of 1701 tracts\n",
      "we have 2 OPPOSING shorted wedges for tract no 1602\n",
      "we have 2 OPPOSING shorted wedges for tract no 1608\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1609 3.0 1 90.0 0.9901 426773.7\n",
      "we have 2 non-opposing shorted wedges for tract no 1617\n",
      "we have 2 non-opposing shorted wedges for tract no 1619\n",
      "I am working on tract number 1620 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1620\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1637 2.0 2 90.0 0.8048 418607.2\n",
      "I am working on tract number 1640 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1654\n",
      "I am working on tract number 1660 of 1701 tracts\n",
      "I am working on tract number 1680 of 1701 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1688\n",
      "we have 2 non-opposing shorted wedges for tract no 1696\n",
      "I am working on tract number 1700 of 1701 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0001032506940188\n",
      "Average and max number of wedgePop loops per tract were:  7.329218106995885 40.0\n",
      "min,average,max of Home District areas were:  0.0 1.21886111206465 3.471107399140319\n",
      "calculated statewide vote was 0.6279376892445943, should have been 0.6182777037206635\n",
      "calcd statewide Hispanic pop was 0.05255062512754726, should have been 0.056777515645449424\n",
      "calcd statewide Black pop was 0.12910957986084698, should have been 0.151823586077969\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.6219870664315109\n"
     ]
    }
   ],
   "source": [
    "#3/7 - 3/10/22 - linear wideAngle, short the opposing wedgePop to match first shorted wedge (see nonper toy)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW 3/7/22\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opposite wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                #now, we \"squish the square\" based on how close we are to boundary - see offline documentation\n",
    "                equivL = (4.*minWedgePop[t] / avgDistrictPop)**0.5  #non-dimensional centroid-boundary distance\n",
    "                wideAngle = avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - equivL)/(levelL - 0.) )  )\n",
    "                wideAngle = min(maxWideAngle, wideAngle)  #prevent too wide of an angle (half-pie)\n",
    "                #HERE, WE ADJUST all wedge ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit to avoid gaping wedges\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                # NEW 3/10/22 - we not only squish the angles, we short the target for the wedge opposite boundary\n",
    "                oppWedgeExpectedPop = minWedgePop[t] * math.tan(0.5*wideAngle)/math.tan(pi/nWedges)  #prediction for new wedge0's pop\n",
    "                oppWedgeExpectedPop = min(oppWedgeExpectedPop,avgDistrictPop/nWedges) #not needed, but just in case...\n",
    "                for nW in range(nWedges):\n",
    "                    if nW == int(nWedges/2):\n",
    "                        targetWedgePop[nW] = oppWedgeExpectedPop\n",
    "                    else:  #NOTE: we don't mess w wedge0's target.  When it shorts again, we will adjust other wedge's tgts\n",
    "                        targetWedgePop[nW] =(avgDistrictPop - oppWedgeExpectedPop)/float(nWedges-1)\n",
    "                # *** END OF 3/10/22 MODIFICATION\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0001032506940188\n",
      "Average and max number of wedgePop loops per tract were:  7.329218106995885 40.0\n",
      "min,average,max of Home District areas were:  0.0 1.21886111206465 3.471107399140319 for  TN\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start TN HD1 5:45, end 6:45\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN 17Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"17Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"maxAngleRatio\",\"levelL\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, maxAngleRatio, levelL]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD2tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1119 1.01 ( -85.2458888405757 35.00415102409686 ) 0.7425109084255671 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1149 0.6797792384278833 2656 pop,GOP 0.06701807228915663\n",
      "1150 0.7360628538149094 2203 pop,GOP 0.11302768951429869\n",
      "1151 0.7131565436712474 3472 pop,GOP 0.6431451612903226\n",
      "1153 0.7895497904848746 714 pop,GOP 0.09663865546218488\n",
      "1154 0.7292823694848782 642 pop,GOP 0.0514018691588785\n",
      "1155 0.7188957221776419 2039 pop,GOP 0.09710642471799902\n",
      "1156 0.6911413354695037 1529 pop,GOP 0.1118378024852845\n",
      "1167 0.7424035901438678 1178 pop,GOP 0.1460101867572156\n",
      "1168 0.7616649002626669 3168 pop,GOP 0.20549242424242425\n",
      "1176 0.7644579033530338 1877 pop,GOP 0.1108151305274374\n",
      "1218 0.7792450650202585 1921 pop,GOP 0.17751171264966165\n",
      "1258 0.763186919954888 2456 pop,GOP 0.17956026058631921\n",
      "1259 0.713266881473028 2090 pop,GOP 0.21291866028708134\n",
      "1261 0.7333391324142601 2524 pop,GOP 0.04556259904912837\n",
      "1262 0.7101178094424522 1899 pop,GOP 0.061611374407582936\n",
      "1263 0.679255056170257 1364 pop,GOP 0.030791788856304986\n",
      "1264 0.68976730523345 2235 pop,GOP 0.03982102908277405\n",
      "1278 0.680800462166204 1689 pop,GOP 0.03493191237418591\n",
      "1279 0.6958708619092346 2417 pop,GOP 0.048820852296235\n",
      "1280 0.7158548614646565 1346 pop,GOP 0.041604754829123326\n",
      "1291 0.7174224215150318 1401 pop,GOP 0.05424696645253391\n",
      "1292 0.6775193109439382 928 pop,GOP 0.04202586206896552\n",
      "1293 0.704301774579046 1795 pop,GOP 0.036211699164345405\n",
      "1305 0.5968742151718176 892 pop,GOP 0.2085201793721973\n",
      "1306 0.7075080828303358 1325 pop,GOP 0.12075471698113208\n",
      "1310 0.670579904841255 1271 pop,GOP 0.03225806451612903\n",
      "1311 0.6563951193897695 949 pop,GOP 0.040042149631190724\n",
      "1312 0.6021507637772417 646 pop,GOP 0.07585139318885449\n",
      "1313 0.6322646667109411 720 pop,GOP 0.02361111111111111\n",
      "1320 0.7316711908115492 1796 pop,GOP 0.11080178173719377\n",
      "1321 0.7662727759607191 1450 pop,GOP 0.04206896551724138\n",
      "1324 0.7602799140753144 1649 pop,GOP 0.10733778047301394\n",
      "1325 0.7808022981770859 1925 pop,GOP 0.0903896103896104\n",
      "1326 0.7401806967026064 1252 pop,GOP 0.03194888178913738\n",
      "1327 0.7613111048061343 1017 pop,GOP 0.06293018682399214\n",
      "1334 0.7099313453999816 1042 pop,GOP 0.6218809980806143\n",
      "1335 0.6107648637318982 1330 pop,GOP 0.40150375939849625\n",
      "1336 0.6814286646847467 2213 pop,GOP 0.07772254857659286\n",
      "1337 0.6563535794265124 3372 pop,GOP 0.26037959667852906\n",
      "1338 0.6331186697345543 3362 pop,GOP 0.26055919095776325\n",
      "1343 0.7406870727033956 2058 pop,GOP 0.034499514091350825\n",
      "1344 0.7346848080471464 1442 pop,GOP 0.04576976421636616\n",
      "1345 0.7030324705178043 2215 pop,GOP 0.04604966139954853\n",
      "1346 0.7069843836272399 1990 pop,GOP 0.043718592964824124\n",
      "1347 0.6737920856275589 1232 pop,GOP 0.041396103896103896\n",
      "1351 0.6665348133404554 1260 pop,GOP 0.07777777777777778\n",
      "1352 0.7231747188965725 1457 pop,GOP 0.04804392587508579\n",
      "1353 0.776620739116217 1547 pop,GOP 0.05042016806722689\n",
      "1357 0.6500356073768938 2248 pop,GOP 0.033807829181494664\n",
      "1358 0.649482515083284 2667 pop,GOP 0.032995875515560553\n",
      "1359 0.6637211651356085 2505 pop,GOP 0.0375249500998004\n",
      "1360 0.6484758880667426 668 pop,GOP 0.08083832335329341\n",
      "1363 0.7638881238824266 1626 pop,GOP 0.07626076260762607\n",
      "1449 0.7580309498464843 2757 pop,GOP 0.8099383387740298\n",
      "1665 0.7908249165769944 3224 pop,GOP 0.5679280397022333\n",
      "1666 0.7854660051874529 2719 pop,GOP 0.6498712762044869\n",
      "1817 0.6367641184195026 10907 pop,GOP 0.7516273952507564\n",
      "1822 0.6531038099239344 10689 pop,GOP 0.7485265225933202\n",
      "1823 0.615482348163038 11504 pop,GOP 0.7515646731571627\n",
      "1833 0.723736299728555 11700 pop,GOP 0.7555555555555555\n",
      "1839 0.7176733241547724 12520 pop,GOP 0.7545527156549521\n",
      "1841 0.09625550202761261 881 pop,GOP 0.8365493757094211\n",
      "1842 0.7048583701458868 12205 pop,GOP 0.754854567800082\n",
      "1873 0.4338333270215339 1442 pop,GOP 0.8169209431345353\n",
      "1874 0.6407521750620779 11582 pop,GOP 0.7449490588844759\n",
      "1876 0.39103180051977066 3372 pop,GOP 0.8075326215895611\n",
      "1882 0.7064234525388866 504 pop,GOP 0.8353174603174603\n",
      "1945 0.7221994519092967 471 pop,GOP 0.821656050955414\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in TN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 1 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13946493628410844 = TN std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1392261569849422 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for TN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  7.8163\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00966 0.62794 HD avg vs true 0.61828\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.03368, should have been 0.03649\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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trqrdwJ8Cv7xB4wRj7BfwUeDHk2xO8mLgR4CzKzxnB+Ps1RdYeKZJkpcD3wecW9Ep147jwM3Du/muA56pqouT3qnPoFZYVV1KcjvwIAvvJLqnqs4kuW24fjfwm8B3An8wPDO4VBvwtyuPuVcajLNfVXU2yV8Bp4GvAx+sqpFvHV7Pxvy39dvAHyV5nIWXsN5VVRvyf8OR5E+ANwBbk5wHfgt4EfzfXp1g4Z18c8CzLDz7nPxxh7cISpLUii/xSZJaMlCSpJYMlCSpJQMlSWrJQEmSWjJQkqSWDJQkqaX/BacyS9eVboe7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for CO\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.01\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbIUlEQVR4nO3df4wc9Znn8fdnZmzsJXYcwsDZjM0QgZKzWNmEkRMvtwSMIcbJQjZRFlubFafcybos3CZE2sho95LjNqdEUSLhW1mbs8hFnHIJEHZRDDg4LIlFuMthxhtwcLCx4zOHMYvH2ZjFAfxj5rk/qtrXM66eqf413VX+vKRW169v1dPl9tPPfOvbXYoIzMysvHo6HYCZmbWXE72ZWck50ZuZlZwTvZlZyTnRm5mVXF+nA8hy/vnnx+DgYKfDMDMrjB07dhyJiP6sdV2Z6AcHBxkeHu50GGZmhSHppVrr3HVjZlZyTvRmZiXnRG9mVnJO9GZmJedEb2ZWcrkTvaReST+X9Eg6/0lJuySNSRqapN0qSXsk7ZO0vhVBm5lZfvVU9J8FXqiafx74OPBkrQaSeoGNwI3AYmCtpMUNxGlmZg3KNY5e0gDwEeA/A58HiIgX0nWTNV0G7IuI/em29wE3A79sPOTa/umtf+LRFx9lLMYqcSN0ehoYN1+ZBgiSn2uu/GxzI/PNtG31saoJjXv93T498d9IEj3qafhRkXWOJluWdX6nWjbVv0UeEcFYjDEWY4zGaPI8NjpuurKusm3lPRGk8zWm64kh13Y5X2M9P4de/f6u53iTHaORdlltam0riV71nn7P9fZUTedY3qMezuk7hw9d/CFmz5hdM9ZG5f3C1N3AF4A5de7/IuDlqvmDwAeyNpS0DlgHsGjRojoPk9i4fSNf3PbFhtqamXXahlUb+LMP/FnL9ztlopf0UeBwROyQdE2d+88q9zM/EiNiE7AJYGhoqKFS6O1Tb9OjHvb++711VceTVf31zrdyX80eq/I6q19zN07X+kulMl2pWCtVbt5HpfKtPh9Z52iyZVnnd6pltfZZj+qKr1IF9qp33HSlEpz4l89U03lVv7ZJt8u5z7z7A8b9JVbP8SY7RiPtstpkbTvufZf+5TXxfThxXfXyt069xfJvLee3J35bM8Zm5KnorwJukrQamAXMlfSdiPhUjrYHgYVV8wPAofrDzK9HPbznXe9p5yHMzFrq+Knjbd3/lBdjI+LOiBiIiEFgDfDjnEke4BngMkmXSJqZtt/ccLRmZla3hsfRS/pDSQeB5cCjkramyxdI2gIQEaeA24GtJCN2HoiIXc2HbWZmedX165URsQ3Ylk4/BDyUsc0hYHXV/BZgSzNBmplZ4/zNWDOzknOiNzMrOSd6M7OSc6I3Mys5J3ozs5JzojczKzknejOzknOiNzMrOSd6M7OSc6I3Mys5J3ozs5JzojczKzknejOzkitVom/0Hp1mZmVWqkQP9d2uzMzsbFC6RG9mZuPlTvSSeiX9XNIj6fx5kh6XtDd9fleNdgck/ULSs5KGWxW4mZnlU09F/1mS2wFWrAeeiIjLgCfS+VqujYilETHUQIxmZtaEXIle0gDwEeCeqsU3A/em0/cCH2tpZGZm1hJ5K/q7gS8AY1XLLoyIVwHS5wtqtA3gR5J2SFpX6wCS1kkaljQ8MjKSMywzM5vKlIle0keBwxGxo8FjXBUR7wduBG6TdHXWRhGxKSKGImKov7+/wUOZmdlEeSr6q4CbJB0A7gNWSPoO8Jqk+QDp8+GsxhFxKH0+DDwELGtB3GZmltOUiT4i7oyIgYgYBNYAP46ITwGbgVvTzW4FfjCxraRzJc2pTAM3AM+3KHYzs1Jp15c+mxlH/1Xgekl7gevTeSQtkLQl3eZC4ClJzwHbgUcj4rFmAjYzKxupvV/07Ktn44jYBmxLp38NXJexzSFgdTq9H1jSbJBmZtY4fzPWzKzknOjNzLpERPf10ZuZWQu0+8cYnejNzErOid7MrOSc6M3MukQ3jqM3M7MWaPc4+lIl+nZdsTYzK7JSJXpo/yejmVnRlC7Rm5kVlcfRm5mVlMfRm5lZU5zozcxKzonezKxLeBy9mVlJeRy9mZk1JXeil9Qr6eeSHknnz5P0uKS96fO7arRbJWmPpH2S1rcqcDMzy6eeiv6zwAtV8+uBJyLiMuCJdH4cSb3ARuBGYDGwVtLixsM1Myuvjo6jlzQAfAS4p2rxzcC96fS9wMcymi4D9kXE/og4AdyXtjMzs1S3jKO/G/gCMFa17MKIeBUgfb4go91FwMtV8wfTZWeQtE7SsKThkZGRnGGZmdlUpkz0kj4KHI6IHQ3sP+tjKvNvk4jYFBFDETHU39/fwKHMzCxLX45trgJukrQamAXMlfQd4DVJ8yPiVUnzgcMZbQ8CC6vmB4BDzQZtZlZGHRtHHxF3RsRARAwCa4AfR8SngM3ArelmtwI/yGj+DHCZpEskzUzbb25J5GZmJdHN4+i/ClwvaS9wfTqPpAWStgBExCngdmAryYidByJiV3Mhm5XcyM9g11eSZ7MWyNN1c1pEbAO2pdO/Bq7L2OYQsLpqfguwpZkgzc4aIz+DH18HoyegdyaseAL6l3c6Kis4fzPWrJsc3pYkeUZh7EQyb2cN/x59Du26kGE2bS64Jqnk1Qs9M5N5sybV1XVTBO3+4oFZW/UvT7prDm9Lknyl22bkZ2cuM8updInerPD6l49P5u63tyaVq+umTf1bZh3lfvuzhn+PPqd2j0c1m3butz8rtLPb2V03Zt2uVr+9WU6lSvQedWOlNbHf3qwO5eu68agbMysoj6M3Myuxdl5fLFWi96gbMysyj7rJyaNuzKyIhNx1Y2ZWZpJc0ZuZlZkr+pw8vNLMiqqdFf2U4+glzQKeBM5Jt38wIr4kaQnwTeAdwAHgjyPinzPaHwDeAEaBUxEx1LLos+L18EozK6BOV/THgRURsQRYCqyS9EHgHmB9RPwu8BDw55Ps49qIWNruJG9mVlQd7aOPxLF0dkb6COC9JJU+wOPAJ9oSYR08vNLMiqrTFT2SeiU9CxwGHo+Ip4HngZvSTT4JLKzRPIAfSdohad0kx1gnaVjS8MjISO4XkLGfhtuamXVKx78wFRGjEbEUGACWSboc+DRwm6QdwBzgRI3mV0XE+4Eb0+2vrnGMTRExFBFD/f399b4OM7PC64rhlRFxlOTm4KsiYndE3BARVwLfA35Vo82h9PkwSV/+smYCnjQ+j7oxs4LqaNeNpH5J89Lp2cBKYLekC9JlPcBfkozAmdj2XElzKtPADSRdPm3jUTdmVkSd/sLUfOAnknYCz5D00T8CrJX0IrAbOAR8Ow12gaQtadsLgackPQdsBx6NiMda/SLMzIqunRX9lOPoI2IncEXG8g3Ahozlh4DV6fR+YEnzYebjUTdmVlSdrugLIwiPujGzQur48MqiiAj30ZtZIbmiz8kVvZkVVTuL1HIlelf0ZlZg7rrJwRW9mRWVu25yckVvZkXli7E5uaI3s6JyRZ+TK3ozKypX9Dm5ojezonJFn5MrejMrKlf0ObmiN7OickWfkyt6Mysqf2EqJ1f0ZlZk7rrJwRW9mRWVu25yckVvZkXli7E5Ba7ozayYOlrRS5olabuk5yTtknRXunyJpJ9J+oWkhyXNrdF+laQ9kvZJWt/qF1AtwhW9mRWT6GzXzXFgRUQsAZYCqyR9ELgHWB8Rv0ty0+8/n9hQUi+wEbgRWExy+8HFLYr9DK7ozayopA523UTiWDo7I30E8F7gyXT548AnMpovA/ZFxP6IOAHcB9zcdNS1Y3VFb2aF1OmKHkm9kp4FDpPcHPxp4HngpnSTTwILM5peBLxcNX8wXZZ1jHWShiUNj4yM5Ax/vLEYo0eluuxgZmeJjlb0ABExGhFLgQFgmaTLgU8Dt0naAcwBTmQ0zSqvM19JRGyKiKGIGOrv788V/EQnx04yo2dGQ23NzDqpa74wFRFHgW3AqojYHRE3RMSVwPeAX2U0Ocj4Sn8AONRYqFM7MXqCmb0z27V7M7O26uSom35J89Lp2cBKYLekC9JlPcBfAt/MaP4McJmkSyTNBNYAm1sU+xlOjp50ojezQup018184CeSdpIk7scj4hGSETQvArtJqvRvp8EukLQFICJOAbcDW4EXgAciYlfrX0bCFb2ZFVU7L8b2TbVBROwErshYvgHYkLH8ELC6an4LsKW5MPNxojezoup0RV8YJ0ZPMKPXF2PNrHg6PryyKE6OuY/ezIrJFX1O7roxs6JyRZ+TE72ZFVU7v9VfukTvL0yZWVG56yaHk6MnfTHWzArJXTc5jcUYPeV6SWZ2lvDF2Dr41yvNrIhc0efUrpNkZtZuruhz8s3BzayoXNHn5JuDm1lRuaKvgyt6MysiV/Q5tevT0Mys3fyFqZzcdWNmReaumxx8MdbMiqqjv0cvaRbwJHBOuv2DEfElSUtJ7io1CzgF/GlEbM9ofwB4AxgFTkXEUMuin8AVvZkVVTsvxk6Z6IHjwIqIOCZpBvCUpB8C/wm4KyJ+KGk18DXgmhr7uDYijrQk4im4ojezIur0HaYCOJbOzkgfkT7mpsvfSRtv+p2XL8aaWVF1uqJHUi+wA7gU2BgRT0v6HLBV0tdJ+vp/r0bzAH4kKYD/GhGbmg+71oHcdWNmxdTx4ZURMRoRS4EBYJmky4HPAHdExELgDuBbNZpfFRHvB24EbpN0ddZGktZJGpY0PDIyUu/rqMTprhszK6Su+cJURBwFtgGrgFuBv0tXfR9YVqPNofT5MPDQJNttioihiBjq7++vJ6xxXNGbWRF1tKKX1C9pXjo9G1gJ7Cbpk/9QutkKYG9G23MlzalMAzcAz7ck8gz+UTMzK6p2Fql5+ujnA/em/fQ9wAMR8Yiko8AGSX3A28A6AEkLgHsiYjVwIfBQ+gL6gO9GxGOtfxkJd92YWZF17GJsROwErshY/hRwZcbyQ8DqdHo/sKT5MPPxxVgzK6qOX4wtElf0ZlZEXXMxttt5HL2ZFZUr+pzcdWNmReWKPidfjDWzonJFb2ZWcq7oc5p7zlx+8/ZvOh2GmVndXNHnNDhvkANHD3Q6DDOzuvkOUzldPO9iXnr9pU6HYWbWEHfd5DD4zkFefv1lRsdGOx2KmVld3HWT08XzLubk2ElePfZqp0MxM6uLL8bmNDhvEICXjrr7xsyKxRV9Tu+e/W4Aj7wxs8JxRZ9T5aq1fwrBzIrGFX1O/lasmRWVK/o6+QYkZlY07SxUS5Xo3XVjZkXWyVsJzpK0XdJzknZJuitdvlTS/5b0bHpT78x7wUpaJWmPpH2S1rf6BYw7lrtuzKygOt11cxxYERFLgKXAKkkfBL4G3BURS4EvpvPjpLcf3AjcCCwG1kpa3JrQa3PXjZkVTUcvxkbiWDo7I31E+pibLn8nyc3CJ1oG7IuI/RFxArgPuLnpqGvwb9GbWVG1s6LPc3PwSmW+A7gU2BgRT0v6HLBV0tdJPjB+L6PpRcDLVfMHgQ/UOMY60huML1q0KG/8mdxHb2ZF0/HhlRExmnbRDADLJF0OfAa4IyIWAncA38pomlViZ76SiNgUEUMRMdTf358r+DMPll6MddeNmRVMp/voT4uIo8A2YBVwK/B36arvk3TTTHQQWFg1P0B2F09LuOvGzIqqoxW9pH5J89Lp2cBKYDdJwv5QutkKYG9G82eAyyRdImkmsAbY3IK4J+WuGzMrmk730c8H7k376XuAByLiEUlHgQ2S+oC3SfvXJS0A7omI1RFxStLtwFagF/hvEbGrHS8E3HVjZsXVzuHhUyb6iNgJXJGx/Cngyozlh4DVVfNbgC3NhZmPu27MrMj8Wzd1cNeNmRVN11yM7Xb+ZqyZFVXHh1eamVl7uaI3Mys5V/RmZiXnit7MrORc0ZuZlZwrejOzkvMdpszMzgLuujEzKzF33dTJv3VjZkXji7E5VU6SvyFrZkXjij6nyknyj5uZWdG4os+pcpJ6VKqXZWZnAVf0OY3FGOCuGzMrHlf0OVU+DV3Rm1nRtLPLecobj0iaBTwJnJNu/2BEfEnS/cB7083mAUfTG4hPbH8AeAMYBU5FxFBLIs9wuqJ3H72ZFVAnbyV4HFgREcckzQCekvTDiLilsoGkbwCvT7KPayPiSJOxTsmjbsysqNrZdZPnVoIBHEtnZ6SP09EoKZ//iOQG4R3lUTdmVlQdvxgrqVfSs8Bh4PGIeLpq9e8Dr0XE3hrNA/iRpB2S1k1yjHWShiUNj4yM5Ax/vErXjfvozaxoOn4xNiJG0/73AWCZpMurVq8FvjdJ86si4v3AjcBtkq6ucYxNETEUEUP9/f35op+4D3fdmFlBiS4ZXhkRR4FtwCoASX3Ax4H7J2lzKH0+DDwELGss1FzxkcbVrkOYmbWF1MGKXlK/pHnp9GxgJbA7Xb0S2B0RB2u0PVfSnMo0cAPwfAvizuSK3syKqp0VfZ5RN/OBeyX1knwwPBARj6Tr1jCh20bSAuCeiFgNXAg8lFbYfcB3I+KxVgU/kSt6Myuqdlb0eUbd7ASuqLHuX2csOwSsTqf3A0uaCzE/V/RmVlS+8UhOrujNrMi64mJst3NFb2ZFFUTbitRyJXpX9GZWUBHRtiK1XIneFb2ZFZQr+pxc0ZtZUbmiz8kVvZkVmSv6HFzRm5mdqVyJ3hW9mdkZypXoXdGbmZ2hXIneFb2Z2RnKlehd0ZuZnaFcid4VvZnZGcqV6F3Rm1lBteuXK6Fsid4VvZkVmL8wlYMrejOzM+W5w9QsSdslPSdpl6S70uX3S3o2fRxIbx6e1X6VpD2S9kla3+L4x3FFb2Z2pjx3mDoOrIiIY5JmAE9J+mFE3FLZQNI3gNcnNkzvSrURuB44CDwjaXNE/LI14Y/nit7M7Ex57jAVwLF0dkb6OH3VQElW/SNgRUbzZcC+9E5TSLoPuBloT6JPw9r/m/3M7ps96TY19zHFD/9P1r6ZtlO170Tb14+/zp4je3jf+e9jzjlzJt3HebPP49LzLp10G2u9iCCI0/+GlenKv3n1+rzLJu6n0X23Yj/1TFfvuzINyV/4kuhRDz3qQSTTWWb1zWL2jNnM6JlBj3ro7elNntV7un1lWeVRBHkq+kplvgO4FNgYEU9Xrf594LWI2JvR9CLg5ar5g8AHGox1SrP6ZgFwy4O3TLGltcOCOQuY2Ttz3AdH9YfMxP+A1cvyLK9n2248XkWPepCU3Ay6iQRp3WGqD4LKB0vl37zyoTNx+sibRzhv9nltiTFXoo+IUWCppHkkN/u+PCKeT1evZcINwqtk9aFkvkslrQPWASxatChPWGdYcckKHl77MG+dfGvS7Sbr2pmqf3+qbqHJ2jfTthPHPvLmEba/sp3lC5dP+gYcizF++tJP+cff/mPm/qqPXVk+1fqa7erYtnp5txyvkqjHYiz5Wdr0P39lu8p//so+Jls2sU2j+2nnvluxn3qmq/ddmY5Iz3d63iuPie/5IHj71Nu8efJNRsdGGY1RxmKM0bHR020qy6qXZy2rXj7x33zidPX8VQuvoh1U7z0KJX0J+G1EfF1SH/AKcGVEHMzYdjnwHyPiw+n8nQAR8ZXJjjE0NBTDw8N1xWVmdjaTtCMihrLW5Rl1059W8kiaDawEdqerVwK7s5J86hngMkmXSJoJrAE21xm/mZk1Ic+VhPnATyTtJEncj0fEI+m6NUzotpG0QNIWgIg4BdwObAVeAB6IiF2tCt7MzKZWd9fNdHDXjZlZfZrqujEzs2JzojczKzknejOzknOiNzMrOSd6M7OS68pRN5JGgJea3M35wJEWhDNdihSvY22fIsVbpFihWPE2EuvFEdGftaIrE30rSBquNdSoGxUpXsfaPkWKt0ixQrHibXWs7roxMys5J3ozs5Irc6Lf1OkA6lSkeB1r+xQp3iLFCsWKt6WxlraP3szMEmWu6M3MDCd6M7PSK0Wil/RJSbskjUkamrDuTkn7JO2R9OGq5bdI2pm2+1qXx7pW0i/SeB+TdH43xippjqRnqx5HJN09HbE2Em+6fKakTZJelLRb0ie6ONZt6bLK+b1gOmJtNN6q9ZslPT9xeTfFmv6/ei5t900lt0/tulgl/Y6kR9P36i5JX811oIgo/AP4l8B7gW3AUNXyxcBzwDnAJcCvgF7g3cD/BfrT7e4FruvSWPuAw8D56XZfI7lrV9fFmtF+B3B1t74P0nV3AV9Op3sq57lLYx237XQ+Gn0vAB8Hvgs8382xAnPTZwF/C6zpxliB3wGuTbeZCfwUuHGq45Sioo+IFyJiT8aqm4H7IuJ4RPwfYB+wDHgP8GJEjKTb/T0wLZVcA7EqfZwrScBc4FCXxnqapMuAC0jeiNOiwXg/DXwlbT8WEdPyzclmzm0nNBKvpHcAnwe+PH2RNhZrRPxzuk0fSQKdllEq9cYaEW9GxE/StieAfwAGpjpOKRL9JC4CXq6aP5gu2we8T9KgkvvefgxYOP3hjZMZa0ScBD4D/IIkwS8GvjX94Y1T67xWWwvcH2np0WGZ8VZukQn8laR/kPR9SRdOe3TjTXVuv5122/yH9IO/0yaL96+AbwBvTndQNUx6biVtJfnr+Q3gwekN7QxT/h9L379/ADwx1c76WhlZO0n6e+BfZKz6i4j4Qa1mGcsiIn4j6TPA/cAY8L9IqvyWaGWskmaQJPorgP3AXwN30qIqqZWxTphfA/xJM7FlHri18faRVEP/MyI+L+nzwNdpUdxtOLd/HBGvSJpD0r3wJ8B/bz7S9MCtfd8uBS6NiDskDbYoxP9/0Da8byPiw5JmAf8DWAE83nSgtCfWtED9HvBfImL/VDEUJtFHxMoGmh1kfKU+QNrtEREPAw8DSFoHjDYbY0WLY12a7vNXAJIeANY3GeJprT6vAJKWAH0RsaPJ8M7Q4nh/TVJtPpQu/z7wb5oKsEob3rOvpM9vSPouSbdDyxJ9i+NdDlwp6QBJnrlA0raIuKbZOKE979t0v29L2kzSddKSRN+mWDcBeyPi7jw7K3vXzWZgjaRzJF0CXAZsB6iMWJD0LuBPgXs6FmWiVqyvAIslVX6V7nqSG613Us3zmlrLhJvGd1hmvGm30sPANel21wG/7EyIp2XGKqlP6Wir9K+8jwLTNpJlErXO7d9ExIKIGAT+Fck1sWs6GCfUPrfvkDQfTlfKq4HdHYwTJs9dXwbeCXwu996m48pyux/AH5J8Ah4HXgO2Vq37C5Ir1nuoujpNkoh+mT6m5Qp7E7H+O5LkvpMkMb27W2NN1+0H3leQ98HFwJPpuX0CWNSNsQLnkoxi2gnsAjaQMdKpW+Kd0HaQ6R11U++5vRB4purc/jXJX6TdGOsASRfOC8Cz6ePfTnUc/wSCmVnJlb3rxszsrOdEb2ZWck70ZmYl50RvZlZyTvRmZiXnRG9mVnJO9GZmJff/AF5jGmOkRCIqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for TN\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 1.052 TN seats out of 9\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the state map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  772147.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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R+fDXp4DNrb4Z+GQbXwV8v/3DrACeBua1aXuA36Hzua2Hgbe1+l8AX2jj64GvtPErgGfa7cI2vnCK/f4RML/VP1mx39E9t/vL6Fz88qOzO0qlnkc9x28FvgUsaNOurNZvj54f6drmTcC/V+iZzofvDwOXtfs7gD+j8H43Ts8l972x+u2H/W66w1w9xHcD8HRm/ojOVylta/VtwM1tfB2wPTNPZeZhOq8c1kTE1cDlmflodv6FHhi1zNl1/QtwQ0QEcCOwKzOfz8z/pvOKce1U+s3MRzLzdKt/l85nxar1e17P7f7ngb/m/A9eV+q5u9/bgDsz8xRAZp4s2O/onhO4vNVfy7nPD1boeT5wWUTMB17Zequ+313Qc/F9r9dzDPX3u2mZqwG1ns7bYICrMvM4QLu9stV7fe3SkjYM96ift0z7j/xT4HXjrGsq/XZ7L51XOdX6Pa/niHgH8Fxmfn/UPJV67n6O3wj8fkQ8FhH/ERG/VbDf0T3fDnw6Io4Cn6FzCOeS95yZz7V+ngWOAz/NzEcovN+N03O3MvveWP32yX43LXMuoNoHgN8BfHWiWXvUcpz6VJcZv4kx+o2IjwGngS9V6rf19oueI+KVwMeAv+k16xS2PxvP8Xw6hyuuA/4K2NFeLZbod4yebwM+nJnL6JyPuHca25+xniNiIZ1X3yuA1wOviog/HW+RKWx7Rp/jiXqutu+N0e+tFN/vZsKcCyg6P+XxeGaeaPdPtLe2tNuzh3PG+tqlYc69te+un7dMe6v9WuD5cdY1lX6JiA3AHwN/0t6KV+p3dM9voLPjfD8ijrR1PR4Rv1Ko59HP8TDwYHbsAX5O50s0q/Tbq+cNwINt/Kt0fhngvO1fop7/EDicmSOZ+X+tx9+l9n43Vs9V971e/b6H+vvd9M3EiaxKA52f8HhP1/1Pc/7J2k+18Ws4/0TiM5w7kfg9Oq+uz55IvKnVN3H+icQdee5E4mE6r8oXtvErptjvWuBJYPGo+Ur026vnUdOOcO5kbYmeezzHfw78XRt/I51DGFGl3zF6Pghc38ZvAPZWeI7p/BrBATrnRYLOeYwPUni/G6fnkvveWP1W3+9mYrjkgTKjD6bzD/gT4LVdtdcBu+lcIrm7+8ml8xb5aTqXYr6tqz5A53LOp4G/59ylmK+g8+p1iM7VML/Wtcx7W32IMf54T7LfITp/MPe14QtV+h2r57F2lAo9j/Ecvxz4p7b9x4E/qNLvOD3/HrCXzh+ex4Brq/QM/C2dy7OfAP6Rzh/GsvvdOD2X3fd69Vt5v5upwa86kiSVNBfPQUmS5gADSpJUkgElSSrJgJIklWRASZJKMqAkSSUZUJKkkv4fTsDSqY2IryoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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q3rPFae37ZJJ2jKFPkqwkecnw/PnA64AvnXda+/6Aydoyyz6Z12rmc1FbLKGU5JeG438GfIL1GTGPAf8FvG2v6r2QCdvyJuCXkzwD/DdwRw3TZDpJ8kHWZ+7sS3IGuJv1N09H1ScTtGMU/QHcDLwV+MLwXgHAu4BrYVR9Mkk7xtAn+4HjWf9Frs8B7qmqj43x3y0ma8vM+sSljiRJLY3tFp8kaUkYUJKklgwoSVJLBpQkqSUDSpLUkgElSWrJgJIktfS/1FeLpcJC8WMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 1.309 1.311\n",
      "fractional expected GOP seats =  7.419  out of  9 seats for TN\n",
      "simpler expected GOP seats =  7.4699  out of  9 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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